I'm a Senior AI Developer / GenAI Developer Specialist focused on building and deploying production-grade AI systems across Generative AI, Agentic AI, Multi-Agent Systems, RAG, MCP, LLMOps, and Responsible AI.

I specialize in designing AI applications that move beyond simple LLM calls into tool-using, context-aware, observable, evaluated, and production-ready systems.

My core engineering stack includes Python, FastAPI, LangChain, LangGraph, MCP, Docker, Kubernetes, Azure, and AWS, with hands-on experience across the complete AI lifecycle — from experimentation and data processing to deployment, evaluation, monitoring, and continuous improvement.

• Agentic AI
• Multi-Agent Systems
• LangGraph
• LangChain
• Tool-Augmented Agents
• ReAct Agents
• Task Routing & Delegation
• Autonomous Workflows
• Dynamic Planning

• Model Context Protocol (MCP)
• MCP Servers
• MCP Clients
• Tool Integration
• Agent-to-Agent (A2A) Communication
• Context-Aware AI Systems

• Production-Grade RAG
• Advanced RAG
• Hybrid Search
• Semantic Search
• Re-Ranking
• Semantic Chunking
• Query Optimization
• Vector Search
• Knowledge Retrieval

• LLM Application Development
• Prompt Engineering
• LLM Fine-Tuning
• PEFT
• LoRA
• QLoRA
• Quantization
• Hugging Face
• OpenAI
• Claude
• Gemini
• LLaMA
• Mistral
• Gemma
• BERT

Building AI systems that are measurable, testable, and production-ready.

• RAGAs
• Promptfoo
• DeepEval
• LLM-as-a-Judge
• Faithfulness
• Relevancy
• Groundedness
• Hallucination Evaluation
• A/B Testing
• Regression Testing
• Automated Evaluation
• Quality Gating

Focused on making AI systems safe, reliable, and enterprise-ready.

• Responsible AI
• AI Guardrails
• Content Filtering
• Content Safety
• PII Redaction
• Prompt Injection Defense
• Toxicity Detection
• Bias Mitigation
• Azure AI Content Safety
• AI Security & Governance

Production AI systems need visibility across application, model, data, quality, latency, and cost.

• Prometheus
• Grafana
• Azure Monitor
• LangSmith
• Langfuse
• Token Tracking
• Cost Tracking
• Latency Monitoring
• Production Logging
• Model Drift Detection
• Data Drift Detection
• AI Quality Monitoring
• LLM Observability

• Supervised Learning
• Unsupervised Learning
• Classification
• Regression
• Clustering
• Anomaly Detection
• Feature Engineering
• Model Training
• Model Evaluation
• Deep Learning
• Computer Vision
• Image Processing
• NLP

• FAISS
• Pinecone
• Qdrant
• Weaviate
• Chroma
• Azure AI Search
• Semantic Search
• Vector Search
• Hybrid Search

• Python
• FastAPI
• REST APIs
• API Integration
• PostgreSQL
• MongoDB
• SQL
• Data Processing
• Data Pipelines
• Backend Architecture

• Azure AI Foundry
• Azure OpenAI
• Azure AI Search
• Azure Machine Learning
• Azure Container Apps
• Azure Kubernetes Service (AKS)
• Azure Container Registry (ACR)
• Azure Container Instances (ACI)
• Azure VMs
• Azure Functions
• Azure Monitor
• Azure AI Content Safety

• EC2
• S3
• SageMaker
• Lambda
• API Gateway
• ECR
• Serverless Deployment

• Docker
• Kubernetes
• CI/CD
• GitHub Actions
• MLflow
• Weights & Biases
• DVC
• Model Deployment
• API Deployment
• Cloud Infrastructure
• Production AI Systems

• LangChain
• LangGraph
• Langflow
• Hugging Face
• Streamlit
• Git
• GitHub
• Postman
• Swagger / OpenAPI
• n8n

An agentic machine learning workflow built with LangChain + LangGraph that automates major stages of the ML lifecycle.

Workflow:

Data Analysis → Preprocessing → Visualization → Model Selection → Training → Testing → Evaluation

The architecture uses specialized agents to route tasks and automate the ML development workflow.

An MCP-based AI system where an LLM agent analyzes uploaded datasets and recommends preprocessing actions while a Python execution agent performs the required transformations.

Workflow:

LLM Agent → MCP → Python Execution Agent → Data Processing

Capabilities include:

• CSV analysis
• Automated preprocessing recommendations
• Data cleaning
• Normalization
• Transformation
• Python-based execution

A RAG-based document intelligence system using:

LLMs + LangChain + FAISS / Weaviate

Capabilities:

• RFP document analysis
• Semantic search
• Scope extraction
• Compliance analysis
• RFP comparison
• Bid / No-Bid decision support

A GenAI + RAG assessment platform built using LangChain.

Capabilities include:

• Question generation from uploaded documents
• Automated assessment creation
• Employee assessments
• Private and shared topics
• Different difficulty levels
• Anti-cheating capabilities

A conversational AI application combining:

ReAct Agents + RAG + FAISS + Hugging Face Embeddings + Web Search + Conversation Memory

Built using LangChain and Streamlit and deployed for real-time conversational use.

Worked on AI systems spanning multiple biometric modalities:

• Face
• Fingerprint
• Iris
• Voice

Focused on secure enrollment, verification, and authentication workflows.

May 2026 – Present | Pune, India

Working on enterprise-grade:

• Agentic AI
• Multi-Agent Systems
• MCP
• Advanced RAG
• LLM Evaluation
• Responsible AI
• Guardrails
• AI Observability
• Azure
• AWS
• Production AI Deployment

Oct 2025 – May 2026 | Ahmedabad, India

Worked on:

• Agentic AI
• MCP
• RAG
• LLM Fine-Tuning
• Multimodal AI
• FastAPI
• Docker
• CI/CD
• Biometric AI

Aug 2024 – Sep 2025 | Ahmedabad, India

Worked on:

• Generative AI
• LLMs
• Agentic AI
• RAG
• Machine Learning
• Deep Learning
• NLP
• FastAPI
• Docker
• Production AI Applications

Before specializing in AI, I worked across IoT, electronics, manufacturing, and industrial systems, giving me a broader engineering foundation and a practical understanding of hardware-to-software systems.

• 🏆 Claude Certified Architect – Professional — Anthropic
• 🏆 Claude Certified Developer – Foundations — Anthropic

• ✅ Generative AI with LLMs — DeepLearning.AI
• ✅ Machine Learning Specialization — DeepLearning.AI
• ✅ Prompt Engineering — DeepLearning.AI
• ✅ RAG and Agentic AI Specialization — IBM
• ✅ Agentic AI with LangChain and LangGraph — IBM
• ✅ AI Agents for Leaders — Vanderbilt University
• ✅ Python Programming Certificate — Google

• Advanced Agentic AI Architectures
• MCP-Based AI Systems
• A2A Communication
• LLMOps
• AI Observability
• Advanced RAG Optimization
• LLM Evaluation & Quality Engineering
• AI Security
• Responsible AI
• Enterprise AI Architecture
• Cloud-Native AI Systems
• Scalable Multi-Agent Systems

Building intelligent systems that can reason, use tools, access knowledge, and operate reliably in production. 🚀