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A. Singh

Raising the citadel…

Ashutosh
Singh

Applied AI engineer. Retrieval, agents and the platforms around them.

Azure AI Engineer and Team Lead with 15+ years building enterprise-scale platforms on Azure. Specialized in RAG architectures, agentic workflows and LLM-powered systems, delivering production-grade copilots and document intelligence pipelines. Today I build agents that do real work for engineers: they read documents, find the evidence, drive CAD tools and know when to ask a person. Most of my fifteen years of code was written by hand.

The citadel is a map of the site. The keep in the middle is me; the camera moves to each building as you read.

Role
Applied AI Engineer and Team Lead
Focus
Agentic AI and multi-agent systems, MCP tools, RAG copilots, document intelligence
Stack
Azure OpenAI, Azure AI Foundry, Azure AI Search, Python, C# and .NET, React and TypeScript
Leadership
Teams of up to 20 across the Bay Area and India; design reviews, coding standards, secure code
Based in
Santa Clara, CA (San Francisco Bay Area)
Education
M.S. Software Engineering, San José State University
documents a month through one agent pipeline
70K+
people using a RAG copilot
2K+
engineers led across delivery teams
20
years building cloud software
15+

Read this site in

AgentMeshPersonal agentsHand-built work

02 / 09

AgentMesh: AI for mechanical engineers

Work, with my team, 2026. The newest work, in the engineering quarter between the keep and the gate.

An agent platform that works inside the CAD tools mechanical engineers already use

Plain English

Mechanical engineers spend their day in CAD and product-data tools, not in chat windows. AgentMesh brings AI to them: engineers open AISD, a portal my team built that runs inside Creo Parametric's own browser panel, and ask for help with the design in front of them.

AgentMesh sends each request to the right specialist agent, from CAD changes and enclosure experiments to battery comparisons, and that agent does the work in the engineer's own tools.

Technical

AISD's agents run in Azure. MCP servers run on the engineer's desktop beside the CAD session (a Creo Parametric MCP, plus Fusion MCP tools), so an agent in the cloud can read and drive the model locally.

Specialist agents and tools share the platform: the Creo CAD assistant, the Enclosure Agent, analysis tools, C-bucket tools and battery tools, alongside other agents in the wider system such as VASIM for virtual simulation.

  1. Engineer in Creo
  2. AISD portal
  3. AgentMesh router
  4. Specialist agent (Azure)
  5. Desktop MCP
  6. CAD / Windchill
  • Azure
  • MCP
  • Creo Parametric
  • Windchill REST API
  • Fusion
  • GitHub Copilot
  • Python

2.1Creo CAD assistant

AgentMesh, 2026

Ask for a CAD change in plain language, inside Creo

Plain English

Engineers describe what they want done to the model and the assistant carries it out in the open Creo session, without leaving the CAD tool.

Next, it will check each requested change and suggest better options before applying it.

Technical

The AISD agent runs in Azure and calls a Creo Parametric MCP server on the engineer's desktop, which performs the operations in the live session.

I'm extending an MCP that an earlier developer started, prioritizing missing capabilities, then adding validation and design suggestions to the CAD interaction.

  1. Request in AISD
  2. Agent plans steps
  3. Creo MCP
  4. Model updated
  5. Validate + suggest
  • Creo Parametric
  • MCP
  • Azure
  • AISD
Runs
Inside Creo

    2.2Enclosure Agent

    AgentMesh, 2026

    Explore an enclosure design before anyone builds it

    Plain English

    Engineers try different enclosure parameters and the agent runs the experiments for them, predicting how stiff each design will be.

    It plots material weight against stiffness, so the best trade-offs are visible at a glance.

    Technical

    The agent runs parameter sweeps on a pre-trained Python surrogate model (deployed as-is), returning predicted stiffness for each variant.

    Results come back as a weight-versus-stiffness Pareto chart.

    1. Design parameters
    2. Parameter sweep
    3. Surrogate model
    4. Stiffness
    5. Pareto chart
    • Python
    • Surrogate model
    • AgentMesh
    Output
    Pareto front

      2.3Battery tools

      AgentMesh, 2026

      Compare laptop battery designs across versions

      Plain English

      Battery decisions depend on how today's design compares with earlier ones. The battery tools pull those versions together so engineers can compare them while they design.

      It is the platform's current focus, aimed at helping engineers make design decisions.

      Technical

      AISD queries the Windchill REST API for battery design data across versions, while Creo talks to Windchill directly for CAD operations.

      1. Question in AISD
      2. Windchill REST API
      3. Battery versions
      4. Comparison
      • Windchill
      • REST API
      • Creo Parametric
      • AISD
      Data
      Windchill

        2.4C-bucket tools

        AgentMesh, 2026

        Laptop C-cover calculations, one prompt away

        Plain English

        The C-bucket is the top of the laptop base: the keyboard and palm-rest enclosure. Engineers can run its calculations straight from GitHub Copilot instead of hunting for a separate desktop tool.

        Next, it becomes a web app like the other agents, with faster calculations.

        Technical

        A GitHub Copilot MCP integration launches a local UI tool and runs the C-bucket calculation on the machine's CPU or GPU.

        Planned: move it from a desktop app to a web app and cut the time each calculation takes.

        1. Copilot
        2. MCP tool
        3. Local UI
        4. CPU/GPU calculation
        • GitHub Copilot
        • MCP
        • Desktop app
        Runs
        From Copilot

          2.5Analysis tools

          AgentMesh, 2026

          Engineering analysis the agents can call

          Plain English

          Analysis tools that the platform's agents call, so their answers rest on calculations rather than guesses.

          Technical

          Exposed to AgentMesh agents as tools alongside the CAD and product-data integrations.

          1. Agent
          2. Analysis tool
          3. Result
          • AgentMesh
          • MCP

            2.6Fusion MCP tools

            AgentMesh, 2026

            The same agents, working in Fusion

            Plain English

            MCP tools that let AgentMesh agents work with Fusion as well as Creo, so the platform isn't tied to one CAD tool.

            Technical

            MCP servers that expose Fusion operations to the platform's agents, in the same pattern as the Creo Parametric MCP.

            1. Agent
            2. Fusion MCP
            3. Fusion
            • Fusion
            • MCP
            • AgentMesh

              03 / 09

              New: agents I build

              Personal systems from this year. I design them and build them with AI coding agents doing much of the typing, and they run every day on free infrastructure. They live in the workshop quarter, east of the keep. Read them in plain English or technical with keys 1 and 2.

              3.1Deals Galore

              Personal, open source, 2026

              A shopping agent that tells me when a deal is real

              Plain English

              Every hour it checks more than fifteen deal sites, brand stores and forums for the things on my list, decides whether each deal is genuinely good, and sends me a Telegram message when one is worth buying.

              It runs entirely on free services, and adding a new item to watch is one tap on its dashboard.

              Technical

              A scheduled GitHub Actions pipeline: RSS, Reddit, Shopify product JSON and schema.org pages feed a keyword prefilter, then batched LLM scoring on Groq with Gemini as fallback, with model discovery and per-model rate-limit handling.

              Deterministic guards run after the model (budget range, brand price floors that catch fakes, per-store caps), so a confident model can't push a bad deal through. A Watch button opens a GitHub issue that a workflow merges into the config.

              1. 15+ sources
              2. Prefilter
              3. LLM scoring
              4. Price guards
              5. Telegram + dashboard
              • Python
              • GitHub Actions
              • Groq
              • Gemini
              • GitHub Pages
              • Telegram Bot API
              Runs
              Hourly
              Hosting cost
              $0

              3.2Paper Desk

              Personal, open source, 2026

              A research agent that reads the new AI papers every morning

              Plain English

              Hundreds of AI papers come out every day. Each morning Paper Desk reads the new ones and the big labs' blogs, keeps what matters for building RAG and agent systems, and writes a short summary of each.

              It also notes why each paper might matter for my work, so I can decide in seconds what to read.

              Technical

              Pulls Hugging Face Daily Papers, the arXiv API and four lab RSS feeds, merges duplicates by arXiv id, and keeps candidates with per-topic keyword hits or high community upvotes.

              An LLM scores relevance against a written reader profile and returns a TL;DR and a 'for you' line as JSON; keyword ranking takes over if the free quota runs out. The page is rebuilt into this site every day.

              1. arXiv + HF + lab blogs
              2. Merge
              3. Topic filter
              4. LLM rank + summary
              5. /papers
              • Python
              • arXiv API
              • Hugging Face API
              • Groq
              • GitHub Actions
              Updated
              Daily
              Topics tracked
              6

              3.3AI Tools

              Personal, open source, 2026

              Skills that teach GitHub Copilot to build AI on Azure properly

              Plain English

              Coding assistants are quick to write AI features and quick to get them subtly wrong. This pack gives GitHub Copilot step-by-step playbooks for RAG, agents, evaluation and security on Azure, and Copilot loads the right one when the task needs it.

              It also includes a scored catalog of open-source AI tools to help choose libraries.

              Technical

              23 Agent Skills (SKILL.md) installable as a Copilot CLI plugin or into .github/skills, organized along one lifecycle: choose, ingest, retrieve, orchestrate, prove, secure, operate.

              Skills enforce keyless Entra ID auth, the Responses API, hybrid retrieval with security trimming, least-autonomy agent design, and evals before claims. Backed by a catalog of 82 OSS tools with verified metrics and licenses.

              1. Choose
              2. Ingest
              3. Retrieve
              4. Orchestrate
              5. Prove
              6. Secure
              7. Operate
              • GitHub Copilot
              • Agent Skills
              • MCP
              • Azure OpenAI
              • Azure AI Search
              • Python
              Skills
              23
              Open-source tools rated
              82

              04 / 09

              Engineered by hand

              Production systems I designed and wrote with my teams, from 2012 to today: the architecture, the code and the late-night fixes were ours. They make up the old town, west of the keep.

              2025 - Present

              Agent Assist - Document Intelligence Platform

              High-volume enterprise documents needed multilingual OCR, classification, extraction, validation, and summarization at scale without overwhelming human agents.

              Solution

              Built a multi-agent Azure AI Foundry workflow that processes 70K+ documents per month through routing, validation, and review checkpoints.

              Architecture

              Document ingress, OCR, language detection, classifier, extraction agents, human review queues, and analytics feedback loops.

              Challenges

              • Latency control across multi-step pipelines
              • Cost-aware OCR and LLM usage
              • Reliable validation for imperfect documents

              Lessons learned

              • Treat evaluation as product infrastructure
              • Use routing and confidence thresholds before invoking expensive models
              • Design agent workflows around clear validation contracts
              • Azure AI Foundry
              • Document Intelligence
              • Azure OpenAI
              • Python
              • Logic Apps
              • Functions
              • Dynamics CRM

              2024 - Present

              Crystal RAG + Copilot Platform

              Knowledge workers needed grounded answers across Teams, CRM, and Office apps with enterprise controls and low-latency retrieval for 2K+ active users.

              Solution

              Delivered a secure Azure RAG platform with hybrid search, grounded citations, and feedback loops for CRM, Teams, and Office experiences.

              Architecture

              Content ingestion, enrichment, Azure AI Search, re-ranking, prompt orchestration, secure API tier, and Teams/Office frontends.

              Challenges

              • Permission-aware retrieval
              • Prompt drift
              • Grounded citation quality
              • Feedback visibility across multiple data sources

              Lessons learned

              • Retrieval quality beats prompt cleverness
              • Caching should be scoped by identity and freshness requirements
              • Analytics need to be part of the copilot product loop
              • Azure OpenAI
              • Azure AI Search
              • Azure AI Foundry
              • Python
              • Teams
              • Office
              • React

              Applied Materials

              EPG Taiwan Plant Enablement

              A new Taiwan plant needed plant-specific workflows, data migration, and reporting support without disrupting existing integrations.

              Solution

              Led backend and data workstreams to extend .NET APIs, SQL Server schemas, and migration flows for Taiwan-specific process rules.

              Architecture

              .NET 6 API extensions, SQL Server schema changes, data validation workflows, migration support, and stakeholder sign-off loops.

              Challenges

              • Plant-specific rule differences
              • Data integrity before go-live
              • Maintaining compatibility with existing integrations

              Lessons learned

              • Process engineers should validate workflows early
              • Schema extensions need explicit consistency standards
              • .NET 6
              • SQL Server
              • Web API
              • Data Migration
              • Process Engineering

              Applied Materials

              Almanac Process Engineering Analytics

              Process engineers needed a single view of operational data across multiple platforms to analyze trends, compare results, and create charts and reports.

              Solution

              Built and supported an analytics experience that integrated plant data into Angular, D3.js, and .NET Web API workflows for faster engineering review.

              Architecture

              Angular application, .NET 6 API, SQL Server and MongoDB-backed data views, D3.js visualization layer, and client validation workflows.

              Challenges

              • Combining data from different systems
              • Interactive chart performance
              • Client-facing support and validation

              Lessons learned

              • Analytics tools need fast feedback from domain users
              • Visualization work benefits from clean API boundaries
              • .NET 6
              • Angular
              • D3.js
              • SQL Server
              • MongoDB

              Enterprise platform rebuild

              Unified Action Tracker

              Large teams were using fragmented ADO workflows that slowed visibility, ownership, and reporting across global operations.

              Solution

              Led a greenfield React and .NET multi-tenant platform that replaced scattered workflows with a dedicated user experience and analytics dashboards.

              Architecture

              React application, .NET API, optimized data model, migration tooling, Redis-backed low-latency paths, and dashboards.

              Challenges

              • Migrating 10K+ records with zero data loss
              • Global reliability expectations
              • Multi-tenant data boundaries

              Lessons learned

              • Migration observability is as important as the migration script
              • Workflow products need strong defaults and escape hatches
              • React
              • .NET
              • SQL
              • Redis
              • Azure App Service
              • CI/CD

              Microsoft contract

              Microsoft 365 Admin Center Feedback Platform

              Product teams needed reliable, low-friction feedback capture inside a globally used admin experience for enterprise IT administrators.

              Solution

              Developed feedback elicitation components and surveys that surfaced user insights in the M365 Admin Center and informed product improvements.

              Architecture

              React and .NET feedback components, SQL-backed feedback flows, Azure deployment pipelines, and product analytics handoff.

              Challenges

              • Global product reliability
              • Non-intrusive feedback UX
              • Turning submissions into actionable product signals

              Lessons learned

              • Feedback capture should respect user context
              • R&D spikes are useful when product behavior is still forming
              • .NET 5
              • React
              • Redux
              • SQL Server
              • Redis
              • Azure
              • Teams

              Microsoft contract

              Tenant Touchpoints

              Sales teams needed a dependable way to track tenants, Microsoft service usage, tasks, comments, and adoption views across a growing customer base.

              Solution

              Delivered React and .NET features for dashboards, task management, and Teams-connected workflows, helping adoption grow from 30 to 5K+ users in two years.

              Architecture

              React and Redux frontend, .NET service layer, SQL Server data model, Azure hosting, and Teams notification exploration.

              Challenges

              • Daily sales workflow reliability
              • Stakeholder triage
              • Feature roadmap feasibility

              Lessons learned

              • Internal tools need product-level support habits
              • Technical feasibility work can shape roadmap decisions
              • .NET 4
              • React
              • Redux
              • SQL Server
              • Azure
              • Azure DevOps
              • Teams

              Cloud architecture case study

              Multi-Region Order Processing Platform

              A customer-facing order system needed resilient, real-time order status across regions.

              Solution

              Architected a multi-region AWS application with load-balanced ECS services and centralized managed RDS.

              Architecture

              Web app, API Gateway, ECS services, IAM controls, RDS MySQL, S3 assets, CloudWatch observability, and regional routing.

              Challenges

              • Cross-region consistency
              • Access control design
              • Operational visibility

              Lessons learned

              • Use managed primitives where they simplify recovery
              • Model order state transitions explicitly
              • AWS ECS
              • RDS MySQL
              • API Gateway
              • S3
              • Lambda
              • IAM
              • CloudWatch

              Rolta India

              Department of Industries Digital Portal

              Small-scale industry processes for Maharashtra Government needed to move from manual submissions to an online registration, claims, and certification workflow.

              Solution

              Designed and built front-end workflows for a public-sector portal that digitized submissions, document handling, and benefit processing.

              Architecture

              .NET MVC portal with jQuery and KnockoutJS views, SQL Server workflows, SharePoint integration, document upload paths, and approval-oriented screens.

              Challenges

              • Digitizing government workflows
              • Document-heavy user journeys
              • Coordinating across a large delivery team

              Lessons learned

              • Public-sector portals need simple task flows
              • Upload and certification steps need clear state models
              • .NET MVC
              • KnockoutJS
              • jQuery
              • SQL Server
              • SharePoint

              Kotak Mahindra Mutual Fund

              Sales Target and Achievements Portal

              Sales teams needed a pan-India system to upload targets and achievements, review performance, and capture manager comments.

              Solution

              Designed and built a custom .NET MVC portal that supported target uploads, manager review, and reporting for a distributed sales organization.

              Architecture

              .NET MVC application with LINQ data access, jQuery interactions, SQL Server storage, target upload workflows, manager review, and reporting views.

              Challenges

              • Requirement discovery with business users
              • Upload validation
              • Manager review and comment workflows

              Lessons learned

              • Operational reporting apps live or die by data validation
              • Customer-facing requirement work improves delivery speed
              • .NET MVC
              • LINQ
              • jQuery
              • SQL Server
              • Reporting

              Angel Broking

              Angel Eye Trading Portal

              A public single-page trading and portfolio management app needed functional fixes, change requests, and performance optimization.

              Solution

              Delivered fixes and enhancements across a .NET MVC and JavaScript application while improving responsiveness for customer-facing trading workflows.

              Architecture

              .NET MVC backend, LINQ-based data access, jQuery front-end interactions, SQL Server persistence, and SPA-style portfolio and trading screens.

              Challenges

              • Customer-facing reliability
              • Performance-sensitive interactions
              • Frequent change requests

              Lessons learned

              • Trading interfaces demand fast feedback loops
              • Small performance fixes matter in heavily used workflows
              • .NET MVC
              • LINQ
              • jQuery
              • SQL Server
              • SPA

              Paramatrix Technologies

              Enterprise Operations Portals

              Multiple businesses needed custom internal systems for training, certification, client records, investment reporting, point-of-sale, inventory, and operational reports.

              Solution

              Built and maintained full-stack .NET MVC applications including Process Tracker, PIE, and Gold POS across finance, certification, CRM-style, and retail operations domains.

              Architecture

              .NET MVC portals with LINQ, jQuery, SQL Server schemas, reporting screens, process workflows, inventory modules, and deployment support.

              Challenges

              • Serving different business domains
              • Balancing new features with production fixes
              • Keeping reporting and transactional data consistent

              Lessons learned

              • Domain modeling transfers across line-of-business apps
              • Reliable deployments are part of full-stack ownership
              • .NET MVC
              • LINQ
              • jQuery
              • SQL Server
              • Inventory
              • Reporting

              Legacy systems project

              Distributed File Storage and Sharing

              Explore distributed storage, replication, leader election, and work stealing in a clustered file service.

              Solution

              Built a Java and Netty-based distributed file sharing system with command queues, work queues, replication, and client messaging.

              Architecture

              Client connection layer, server state manager, command/work handlers, leader election, data replication, and queue orchestration.

              Challenges

              • Cluster coordination
              • Message routing
              • Replication during topology changes

              Lessons learned

              • Operational complexity grows at the edges
              • Queues need first-class observability
              • Java
              • Netty
              • Protocol Buffers
              • Redis
              • Distributed Systems
              Source on GitHub

              05 / 09

              Where I've worked

              Each employer is a tower on the wall, oldest beside the gate.

              Dec 2018 - PresentTeam Lead / AI EngineerTech MahindraMicrosoft and Applied Materials contract programs
              • Led current Microsoft and Applied Materials contract programs spanning Azure AI, RAG, copilots, analytics, and enterprise platform modernization.
              • Designed a multi-agent document intelligence system processing 70K+ documents per month across OCR, multilingual classification, translation, extraction, summarization, and validation.
              • Led RAG and copilot delivery for CRM, Teams, Office, and React experiences serving 2K+ active users.
              • Engineered agentic workflows orchestrating multi-step LLM reasoning chains, cutting manual effort and turnaround time at scale.
              • Managed engineering teams of up to 20 people across the San Francisco Bay Area office and an offshore team in India, running design reviews, unblocking delivery, and mentoring engineers on React, .NET, Azure, and AI patterns.
              • Led initiatives across the company to reduce technical debt, improve application performance, and introduce coding standards, secure coding practices and a code review process.
              • Developed scalable React and .NET applications, Teams extensions and copilots, growing adoption from 30 to 5,000+ users.
              • Owned Azure infrastructure across App Services, Functions, Logic Apps, Key Vault, Redis, Blob Storage, Azure AI Search, Azure AI Foundry, and CI/CD pipelines.

              Event-driven Azure platform with API orchestration, retrieval pipelines, secure document processing, and telemetry-backed feedback loops.

              • OpenAI
              • Azure AI Search
              • Azure AI Foundry
              • .NET
              • React
              • Python
              • Logic Apps
              • Redis
              Summer 2017Software Engineer InternIcertisEnterprise workflow automation
              • Designed chatbot integrations for REST services accessible from email, Microsoft Teams, and SMS.
              • Delivered workflow automation and platform integrations across systems including Workday and ServiceNow.

              Conversational integration layer over enterprise workflow APIs.

              • Bots
              • REST APIs
              • Teams
              • ServiceNow
              • Workday
              Aug 2015 - Jul 2016Senior Software EngineerCitiusTechHealthcare software platforms
              • Built and stabilized healthcare provider applications across billing, revenue management, OASIS review, and clinical compliance workflows.
              • Led architecture and exception-handling improvements that reduced exception rates by roughly 98%.
              • Improved performance and code quality in a legacy .NET MVC, AngularJS, jQuery, and SQL Server application stack.

              Stabilized service and application layers with consistent error handling contracts.

              • .NET MVC
              • AngularJS
              • jQuery
              • SQL Server
              • Enterprise Web Apps
              Nov 2014 - Jul 2015Software Engineer L2Rolta IndiaGovernment digitization
              • Built front-end and workflow functionality for a Maharashtra Government Department of Industries portal.
              • Helped digitize registration, document upload, certification, and approvals for small-scale industry processes.
              • Guided team members through implementation issues across .NET MVC, Knockout, jQuery, SQL Server, and SharePoint.

              Transactional web systems for secure public-sector workflows.

              • .NET MVC
              • KnockoutJS
              • jQuery
              • SQL Server
              • SharePoint
              Aug 2012 - Nov 2014Software EngineerParamatrix TechnologiesEnterprise operations
              • Delivered finance, trading, certification, reporting, CRM-style, and point-of-sale applications across multiple client domains.
              • Built full-stack .NET MVC portals using LINQ, jQuery, SQL Server, reporting workflows, and customer-facing requirement discovery.
              • Handled deployments, production fixes, performance optimization, and feature delivery alongside cross-functional teams.

              Line-of-business applications backed by relational data models and reporting workflows.

              • .NET MVC
              • LINQ
              • jQuery
              • SQL Server
              • Reporting

              06 / 09

              Systems, with the tradeoffs shown

              Pick a system, then a stage, to see why each part was chosen, what it costs and how it is secured.

              Permission-aware retrieval with hybrid search, re-ranking, citations, and answer evaluation.

              Stage 1 of 4

              Identity Gateway

              Why this
              Enterprise search without ACL trimming leaks data.
              Alternatives considered
              App roles, Graph connectors
              Pros
              Central policy point
              Cons
              Requires careful cache scoping
              Performance
              Token validation and permission lookups must be cached carefully.
              Cost
              Identity lookups can dominate latency if not batched.
              Security
              Use Entra ID, Key Vault, managed identities, and per-user audit trails.

              07 / 09

              What colleagues say

              • Ashutosh led a vendor team of 4 people to work on a challenging web project, reporting directly to me. The project was to build a web SDK in React with both front-end and back-end components. His excellent work not only contributed tremendously to the success of the project, but also helped me offload some of my responsibilities. He would be a great asset in any organization.

                J.P. Zhang

                Principal Software Engineer at Microsoft

              • Ashutosh is a great co worker to work with. Help other people in the team when they are struck. Takes full ownership of work that he is working. Always thinks future before writing code. Always follows best practices.

                Srinivas Naidu Kondisetty

                Team Lead at TechMahindra

              • Ashutosh is an extremely enthusiastic and motivated developer, who is passionate about technology and is always up-to-date with the latest trends. He picks up new technology easily and is very good at training others how to use it. It was always a pleasure to work with him as he is friendly, helpful, charismatic and always wears a smile. I would recommend Ashutosh to anyone and everyone.

                Ravindarreddy Umentala

                Tech Lead at CitiusTech

              • Hardworking and sincere. Extremely reliable and most of the critical tasks in the project were executed by Ashutosh. Technically a strong individual and always helping his team. Would like to work with him again in future.

                Lalit Singh

                AVP at CitiusTech

              • Ashutosh was highly valued team member while I acted as onsite team lead for couple of assignments for a US client. I found him very hard working and technically sound and a great team player. He is a quick learner and at the same time outperforming with his existing skills. Wish him all the best for his future assignments!

                Zahirabbas Rupani

                Technical Specialist - CSM

              • Ashutosh is a focused and committed individual. He ensures that the deadlines are never missed and quality is not compromised. He is an excellent team player, and understands the client business problems quite well.

                Prasun Datta

                Executive Manager, Rolta

              • Ashutosh is very dedicated and hard working person with very good technical skills. He has helped other colleagues in office, showing his sincerity and generosity.

                Vineet Singh

                Tech Lead, CitiusTech

              • Ashutosh has a very good capability of resolving all critical issues faced by the team of 10 developers. He has always kept work on priority and ensured successful completion with dedication. He is technically very sound and an excellent team player. I wish him good luck.

                Shyam Saraf

                Tech Lead, L&T Infotech

              08 / 09

              Tools I use

              Agents
              Multi-agent orchestration, Model Context Protocol (MCP) servers, Tool calling, GitHub Copilot extensions, CAD automation (Creo, Fusion), LLM evaluation
              AI
              Azure OpenAI, RAG, LLM Pipelines, Prompt Engineering, Agentic Workflows, Document Intelligence
              Cloud
              Azure App Services, Azure Functions, Azure AI Search, Key Vault, Blob Storage, AWS ECS, RDS
              Backend
              C#, .NET Core, ASP.NET MVC, Python, Node.js, REST APIs, SQL
              Frontend
              React, TypeScript, JavaScript ES6, Redux, Angular, D3.js, HTML5
              DevOps
              Docker, CI/CD, CloudWatch, Postman, Application Monitoring
              Databases
              Cosmos DB, MongoDB, Redis, MySQL, SQL Server

              Certifications

              • GitHub Copilot (Microsoft, 2026)
              • Microsoft Azure AI Engineer Associate
              • Microsoft Azure Developer Associate
              • MCSD: Microsoft Certified Solutions Developer
              • MCSA: Web Applications (2016)
              • Microsoft Azure Fundamentals
              • Microsoft Power Platform Fundamentals

              Education

              • M.S. Software Engineering (cloud computing and enterprise software), San José State University, 2016–2018. GPA 3.74, Phi Kappa Phi, ACM
              • B.E. Computer Engineering, Don Bosco Institute of Technology, University of Mumbai, 2007–2011. IBM technical contest prize winner

              Keeping up: I read new research through Paper Desk, one of the agents above. I also keep learning paths for the agent stack: MCP, A2A, context engineering, evals, observability and security, and a daily news digest covering tech, India, Canada and the US. Need a break? Play a round of Moon Golf, nine holes in one-sixth gravity with a new course every day.

              09 / 09

              Let's build AI that holds up in production

              I'm in Santa Clara, CA, working on enterprise AI: retrieval, agent workflows, Azure architecture and the full-stack apps around them. The quickest way to reach me is email.

              ashutoshsingh1989@gmail.com
              LinkedIn GitHub Download resume