nabeel

– Agentic AI Systems (LangChain/LangGraph): Designed multi-agent workflows for static analysis, PR review, schema validation, and risk detection using LangChain and LangGraph-style orchestration patterns (state graphs, tool execution, retries, failure boundaries). Automated 60% of code review workload. – RAG Retrieval Architecture: Built a PGVector-based RAG system indexing logs, traces, diffs, and outages with hybrid search, embeddings, re-ranking, and structured metadata routing. Improved engineering root-cause discovery and reduced MTTR by 20%. – Python AI Services: Developed FastAPI microservices powering retrieval, model routing, embeddings pipelines, and agent inference. Implemented async workers, cache layers, and circuit-breakers improving reliability under high load. – LLM Fine-Tuning & Prompt Optimization: Performed supervised fine-tuning, prompt compression, system instruction optimization, and evaluation loops for internal classification, summarization, and debugging tasks, improving output consistency and reducing hallucination rates. – Multi-Model Routing: Integrated OpenAI (GPT-4/4o), Anthropic Claude, and Gemini with cost-aware routing, fallback strategies, rate limiting, and context-window optimization. – LLM Evaluation & Monitoring: Built automated eval pipelines measuring retrieval precision, latency, cost per request, hallucination cases, and model drift across real production traffic. – Distributed Backend Engineering: Built high-throughput WebSocket and event-driven pipelines, optimized p95 latency by 35%, and deployed serverless architectures (AWS Lambda, ECS, SNS/SQS).

– AI-Assisted Legacy Modernization: Built LLM-driven debugging and RAG tools mapping production failures to historical patterns, accelerating system modernization cycles. – LLM Test Automation: Generated contract tests, integration tests, and schema-alignment checks using LLMs improving coverage and significantly reducing regression bugs. – Scalable Backend Systems: Delivered high-availability services powering 5M monthly EdTech users with 99.9% uptime (Node/Python/Postgres/Redis). – Performance Engineering: Resolved bottlenecks using profiling, caching, query optimization, and distributed tracing eliminating 95% of slow-path issues.