Professional Overview
10+ years full-stack engineering, 7+ years deep in Gen AI - GPT-4o, Claude, Gemini, AWS Bedrock. Built RAG pipelines, Agentic workflows & LLM products across healthcare, finance & real estate. Stack: Python, FastAPI, .NET, React, LangChain, LangGraph, Qdrant. Architecture to production - I own it all.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Architected production-grade Agentic RAG platform on AWS ECS using Bedrock & Qdr
- Designed multi-agent LangGraph workflows with memory & tool-calling.
- Integrated AWS Bedrock Agents for live data retrieval & automation.
- Built LLM observability pipeline via LangSmith & Arize Phoenix.
- Standardized Docker/K8s deployments with Terraform IaC.
Key Achievements / KPIs
- Reduced query response time by ~60% via Agentic RAG platform.
- Cut LLM inference costs by ~42% via prompt optimization & Redis caching.
- Improved stability with structured logging, tracing & eval harnesses.
- Owned full AI lifecycle — prompt design to cloud deployment.
Education
Field of Study: Computer Science
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
