Senior Backend Engineer with 10+ years building production-grade systems that scale. I specialize in high-performance Python APIs, Agentic AI systems, and cloud-native infrastructure that powers intelligent applications serving millions of users.

What I Build:

• High-Performance Backend APIs → FastAPI + Pydantic, async patterns, microservices, handling 10k+ req/min
• Agentic AI Systems → Multi-agent orchestration, autonomous workflows, LangGraph, CrewAI, AutoGen
• Intelligent LLM Applications → RAG pipelines, tool-calling agents, semantic search at scale
• Modern Web Applications → React, Next.js, TypeScript frontends with scalable backends
• Full-Stack Solutions → End-to-end development from responsive UIs to production APIs
• Test-First Architecture → 90%+ coverage, TDD, automated CI/CD, zero-downtime deployments
• Cloud Infrastructure → GCP, AWS, Kubernetes, Docker, Infrastructure as Code

• Built high-performance FastAPI backends processing 10k+ req/min with async patterns and Pydantic validation
• Deployed autonomous multi-agent systems processing complex workflows with LangGraph and CrewAI
• Created tool-calling agents with dynamic function execution and real-time decision-making
• Implemented RAG pipelines with 95%+ accuracy using vector search and semantic retrieval
• Built agent orchestration frameworks managing concurrent agent interactions and state
• Designed memory-enhanced agents with long-term context retention and learning capabilities

• Reduced operational costs by 40% through intelligent automation and AI integration
• Built full-stack applications serving 10M+ monthly active users with 99.9% uptime SLA
• Decreased time-to-market by 60% with robust test automation and CI/CD pipelines
• Improved customer satisfaction scores 35% via AI-powered interfaces and support systems

• Sub-200ms response times on semantic search across 100GB+ datasets
• Zero-downtime deployments for microservices handling billions of events
• Modern UI/UX with React, Next.js, and Tailwind CSS delivering pixel-perfect experiences
• Automated test coverage enforced at 90%+ across frontend and backend services
• Cost-optimized infrastructure reducing cloud spend while improving performance

Python (FastAPI, Django, Flask) | Pydantic V2 | GraphQL | REST
Async/Await Patterns | WebSockets | gRPC | Message Queues
Node.js | Express | NestJS | Microservices Architecture
PostgreSQL | MongoDB | Redis | Qdrant | Elasticsearch

LangGraph (Agent Workflows) | CrewAI | AutoGen | Agents Framework
OpenAI GPT-4o/o1 | Anthropic Claude | Multi-Agent Orchestration
LangChain | Tool-Calling | Function Calling | Agent Memory
Hugging Face Transformers | Vector Embeddings | RAG Pipelines
Semantic Search | Prompt Engineering | Agent State Management

React | Next.js | Vue.js | TypeScript | JavaScript
Tailwind CSS | Styled Components | Responsive Design
Redux | Zustand | React Query | State Management
Vite | Webpack | Modern Build Tools

GCP (GKE, Cloud Run, BigQuery) | AWS (Lambda, ECS, RDS)
Heroku | Vercel | Railway | Serverless Deployment
Kubernetes | Docker | Terraform | ArgoCD
GitHub Actions | GitLab CI/CD | Monitoring (Datadog, Grafana)

Pytest | Jest | Vitest | Cypress | Playwright
TDD/BDD | Integration Tests | E2E Testing
Coverage.py | Mock/Patch Strategies | CI/CD Automation
Performance Testing (Locust, k6)

Backend Engineering & Agentic AI Systems:

• Multi-Agent Orchestration → LangGraph workflows, CrewAI teams, agent-to-agent communication
• Autonomous Agents → Tool-calling, function execution, decision-making, self-healing systems
• Agent Memory & State → Long-term memory, context retention, RAG-enhanced agents
• Advanced RAG → Hybrid search, reranking, query optimization for production systems
• LLM Observability → Tracing, monitoring, and optimizing agentic applications
• Vector Databases → Qdrant, Pinecone, Weaviate for semantic search at scale
• High-Performance APIs → FastAPI async patterns, Pydantic validation, microservices architecture

Modern Full-Stack Development:

• React Ecosystem → Next.js 14+, Server Components, App Router, RSC patterns
• TypeScript Excellence → Type-safe full-stack applications with end-to-end typing
• Modern UI → Tailwind CSS, Shadcn/ui, Framer Motion, responsive design systems
• Frontend Performance → Code splitting, lazy loading, SSR/SSG optimization
• API Integration → GraphQL, REST, tRPC, real-time WebSocket connections

Cloud-Native Architecture:

• Infrastructure as Code → Terraform, Pulumi for reproducible deployments
• GitOps Workflows → ArgoCD for Kubernetes application delivery
• Observability → Distributed tracing, metrics, and log aggregation
• Cost Optimization → Right-sizing, spot instances, serverless patterns

✅ Clean Architecture → SOLID principles, dependency injection, layered design
✅ API Design → RESTful standards, GraphQL efficiency, versioning strategies
✅ Security First → OAuth2, JWT, input validation, rate limiting, encryption
✅ Async Patterns → Non-blocking I/O, concurrent processing, queue-based workflows
✅ Type Safety → Pydantic models, mypy strict mode, runtime validation
✅ Documentation → OpenAPI/Swagger, code comments, architecture diagrams
✅ Monitoring → Structured logging, metrics, alerts, distributed tracing

✅ Agent Design → Clear roles, bounded autonomy, fallback mechanisms
✅ Tool Orchestration → Dynamic tool selection, error handling, retry logic
✅ Memory Management → Efficient context windows, vector search, state persistence
✅ Agent Observability → Tracing agent decisions, logging tool calls, debugging workflows
✅ Safety & Guardrails → Input validation, output verification, cost controls

✅ Component-Driven UI → Reusable React components, design systems, Storybook
✅ Type Safety → TypeScript strict mode, end-to-end type safety
✅ Test-First Development → Jest, Pytest, Cypress, maintain 90%+ coverage
✅ Responsive Design → Mobile-first, accessibility (a11y), WCAG compliance
✅ Performance Optimization → Core Web Vitals, lighthouse scores, bundle optimization

I'm interested in roles that combine:

• Backend Engineering → Building high-performance Python APIs with FastAPI and microservices architecture
• Agentic AI Engineering → Designing autonomous multi-agent systems and intelligent workflows
• LLM Systems Architecture → Production-grade AI applications, RAG pipelines, and semantic search at scale
• Full-Stack Development → Modern web applications with React/Next.js and Python backends
• Technical Leadership → Mentoring teams and driving engineering excellence
• Innovation → Exploring cutting-edge technologies in AI, agents, and cloud-native development

Ideal Environments:

• Product-driven organizations building AI-first applications
• Teams that value testing, type safety, and code quality
• Projects leveraging agentic AI and LLMs to solve complex problems
• Remote-friendly, collaborative cultures with modern tech stacks
• Companies investing in autonomous systems and intelligent automation