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// about

About me

AI Engineer with 3+ years building production Generative-AI systems end-to-end at CloudLex — agentic platforms, RAG, voice AI, and LLMOps on Azure. I design Python backends for LLM orchestration (async workers, queues, and distributed RAG pipelines) and own delivery across the full life cycle: design, development, deployment, and monitoring — with a focus on reliability, guardrails, and cost.

The thread through my work is owning systems end-to-end — I'm most useful when I can take something from a vague problem to a deployed, monitored service, and I care as much about guardrails, cost, and reliability as about the model itself.

// how I work

End-to-end, for real

Design

System architecture, data models, and the agent / RAG design that decides whether a feature is reliable or flaky.

Build

Python backends for LLM orchestration — async workers, queues, APIs — plus the frontends when needed.

Ship

Docker, Azure Container Apps / Functions, CI/CD and IaC, so it deploys many times a day without downtime.

Operate

Observability, guardrails, evals, and cost/latency tuning — keeping the system honest in production.

// stack

Skills

Languages

  • Python
  • TypeScript
  • SQL
  • C++

GenAI & Agentic

  • RAG & GraphRAG
  • Multi-agent orchestration
  • LangChain
  • LangGraph
  • LiteLLM
  • Prompt engineering
  • Evals & guardrails
  • Embeddings & vector search

LLM Providers

  • Azure OpenAI (GPT-4o)
  • Anthropic Claude
  • Google Gemini
  • Groq
  • Hugging Face

Voice & Document AI

  • Azure Communication Services
  • Azure Speech (STT/TTS, SSML)
  • Azure AI Search
  • OCR

Backend & Data

  • FastAPI
  • Azure Functions
  • Async workers & queues
  • Service Bus
  • PostgreSQL + pgvector
  • Cosmos DB
  • MySQL
  • Redis
  • REST APIs

Cloud, DevOps & LLMOps

  • Azure Container Apps
  • Container App Jobs
  • Docker
  • Azure DevOps CI/CD
  • Bicep
  • Application Insights / KQL
  • Cost & observability

Foundations

  • PyTorch
  • Transformers from scratch
  • Data Structures & Algorithms
  • System design
// experience

Experience

  1. Software Engineer II, Generative AI · CloudLex

    Jan 2023 – Present
    • Built a GraphRAG pipeline (LiteLLM + Cosmos DB on Azure) indexing 10K+ legal documents — improving retrieval precision ~35% and cutting latency ~40%.
    • Automated model-deployment pipelines via Azure DevOps CI/CD, enabling zero-downtime rollouts across multi-tenant environments.
    • Developed multiple LLM chatbots (helpdesk, multi-tenant intake, PIP/MVA) with FastAPI and LangChain, automating client intake and cutting manual workload ~50%.
    • Created a natural-language voice/IVR system with Azure Speech and PSTN integration that handles 400+ monthly calls autonomously, reducing response time ~60%.
    • Designed multi-agent LLM systems for summarization, document review, and task generation using state-machine orchestration and Redis caching.
    • Optimized token usage, API throughput, and inference speed — cutting cloud costs ~22% — and maintained 99.9% uptime across the GenAI products.

  2. Software Developer Intern · Eaton

    Jun 2022 – Jul 2022
    • Built and optimized Angular UI modules for customer management and standardized REST API integration, improving render performance.
// background

Education & achievements

Education

  • B.E. Computer EngineeringJul 2019 – Jul 2023
    Pune Institute of Computer Technology (PICT)
    CGPA 9.08 / 10
  • Higher Secondary (XII)Jun 2017 – May 2019
    Deulgaon Raja Junior College
    82.77%

Achievements

  • Smart India Hackathon 2022 — National Finalist
  • Solved 620+ LeetCode problems
  • CodeChef rating 1801
  • MHT-CET — 99.71 percentile (Top 1%)