I am a Senior Backend Engineer & AI/ML Specialist with 10+ years of experience designing and building scalable backend systems, cloud-native applications, and AI-powered solutions.

I have deep expertise with FastAPI + Pydantic (schema validation, settings management, type-safety) and follow test-first/TDD approaches for production pipelines. I combine Python backend mastery with LLM/AI integration, microservices architecture, and DevOps automation to deliver high-performance software used at scale.

✔️ Python & FastAPI Mastery with Pydantic-based validation and schema-driven design
✔️ Test-Oriented Development → Pytest, TDD, CI/CD, mocks, coverage-driven quality
✔️ AI/ML Engineering → GPT‑4/3.5, LangChain, Hugging Face, Retrieval-Augmented Generation (RAG)
✔️ Cloud-Native Dev → Heroku, GCP, AWS, Vercel, Docker, Kubernetes, CI/CD automation
✔️ Databases Expertise → MongoDB, PostgreSQL, Redis, Qdrant (Vector DB)

• Backend Development → Python (FastAPI, Django, Flask), Pydantic, async APIs, REST/GraphQL
• Testing & QA → Pytest, TDD, integration tests, CI/CD + coverage enforcement
• AI/ML → LLMs (OpenAI GPT, LangChain, Hugging Face), embeddings, vector retrieval
• Database Systems → MongoDB, PostgreSQL, Redis, Qdrant
• Cloud & DevOps → GCP, Vercel, AWS, Heroku, CI/CD, Docker, K8s

• Advanced FastAPI REST & GraphQL API design with async scaling
• Strong Pydantic usage for validation, schema management, settings, and domain modeling
• Clean architecture, SOLID principles, high-availability design

• Pytest-first approach with CI/CD pipelines
• TDD methodologies ensuring accelerated, failure-resistant delivery
• Achieved 90%+ code coverage across production backends
• Structured test suites: unit, integration, service, and E2E

• Built LLM-powered backends with OpenAI GPT, LangChain, embeddings-based RAG
• Semantic retrieval systems with Qdrant & Redis vector search
• Deployed enterprise RAG with sub‑200ms response across 100GB+ datasets

• MongoDB → aggregations, replicas, scale-out design
• PostgreSQL → performance tuning, partitioning, replication
• Redis → caching, distributed locks, pub/sub
• Qdrant → vector-based semantic search for LLM embeddings

• Heroku → rapid builds & team workflows
• GCP → GKE, BigQuery, CloudRun for scalable ML apps
• AWS → Lambda, ECS, EC2, IAM, autoscaling infra
• Vercel → fast deployment of serverless APIs
• CI/CD → GitHub Actions, GitLab CI, automated tests + deployments
• Kubernetes / Docker → container orchestration for microservices

• FastAPI-based analytics engine with Pydantic models
• Natural language query to GPT-4 → real-time insights for clients
• Served 10k+ requests/minute with async optimization
• Automated Pytest + CI/CD with 90% coverage

• Built LangChain + Qdrant + MongoDB AI retrieval system
• Reduced support tickets by 40% via intelligent automation
• Maintained sub-200ms retrieval speeds across 100GB+ dataset

• Designed FastAPI + Pydantic-based microservices deployed via K8s
• Full test-oriented CI/CD pipelines baked in
• Achieved 99.9% uptime with RabbitMQ event-driven distribution

• Leveraging Pydantic v2 for optimized backend data validation
• Test-first architecture with TDD & CI/CD pipelines
• Building next-gen AI-powered microservices backends
• Scaling semantic AI search with Qdrant & Redis vectors

💡 Open to roles in:

• Backend Engineering (Python FastAPI + Pydantic)
• AI/ML & LLM-focused system design
• Test-Oriented project leadership
• Database and Cloud-native architecture