Ajay Desai

About

Backend/AI engineer with experience in both high-scale financial services (JP Morgan trade compliance analytics API on Java/Spring Boot/Postgres/Elasticsearch on AWS EKS processing 1M+ trades/day) and applied LLM systems for legal research (LangChain/OpenAI + Weaviate semantic search). Demonstrated strength in reliability/performance engineering, data consistency during migrations, and production-grade workflow orchestration with observability and human-in-the-loop guardrails.

Experience

Education

Key Strengths

• Scaled backend API handling 1M+ trades/day on AWS EKS
• Major performance win by shifting analytics queries from PostgreSQL to Elasticsearch (hours to <30 min; 40% DB load reduction)
• Data consistency strategy for system migration (versioning/timestamps, parallel run validation to 100% match)
• Shipped LLM-powered legal research ingestion/summarization with semantic search (LangChain/OpenAI/Weaviate) end-to-end
• Reliable workflow/agent-style pipeline design with resumable steps, retries/backoff, and manual review escalation (80% fewer failed runs)
• Strong production Python engineering practices (typing/mypy, async, DI, testing, linting, observability with Prometheus)
• PostgreSQL performance tuning (composite indexes, monthly partitioning, window functions) cutting queries 12s to <3s
• Designed scalable async FastAPI microservices for document ingestion and AI orchestration
• Database performance optimization via indexing and batching (reduced nightly batch from 4+ hours to under 2 hours)
• Production RAG architecture integrating OpenAI + LangChain with a vector DB for grounded legal research answers
• Multi-tenant data isolation patterns for healthcare systems with HIPAA-driven security (RBAC + encryption)
• Built Kafka-based streaming pipeline handling 10,000–12,000 records/min with near-instant vital sign updates