Key Responsibilities :

- AI Architecture & Agentic Engineering : Design and implement stateful, multi-step LLM workflows and agentic architectures using LangChain and LangGraph. Conduct advanced prompt engineering, RAG (Retrieval-Augmented Generation) optimization, and semantic search routing.

- Backend Development & APIs : Build high-performance, event-driven microservices and RESTful/GraphQL APIs using TypeScript, Node.js, and Express.js.

- Data Management & Storage Strategy : Architect data pipelines using PostgreSQL for relational data, Redis for caching/state management, MongoDB for document storage, and Vector Databases (e.g., Pinecone, Qdrant, PGVector) for high-dimensional embeddings.

- Multi-Cloud AI Platform Integration : Connect, orchestrate, and deploy enterprise AI models using Azure OpenAI, OpenAI API, and AWS Bedrock.

- Quality Assurance & Test Automation : Build resilient, fully automated test suites using Jest for unit/integration testing and Playwright for end-to-end API and UI system validation.

- Production Operations & Cloud Infrastructure : Deploy, scale, and maintain cloud infrastructure on Azure and AWS ensuring security, enterprise compliance, rate limiting, low latency, and cost efficiency.

Required Qualifications & Experience :

- Bachelors or Masters Degree in Computer Science, Software Engineering, Applied Mathematics, or a related quantitative field.

- 5+ years of professional software engineering experience delivering scalable backend systems in Node.js and TypeScript.

- 2+ years of hands-on experience building and deploying production-grade AI applications powered by LLMs, vector search, and agent orchestration frameworks.

- Proven track record of managing vector embeddings, mathematical distance calculations, and prompt-to-context window optimizations.

- Experience with cloud-native design, CI/CD pipelines, containerization (Docker), and automated testing strategies.

Preferred Qualifications :

- Experience with guardrails and evaluation frameworks for LLMs (e.g., Ragas, TruLens, LangSmith).

- Knowledge of serverless architecture and micro-frontend/API gateway patterns.

- Experience handling enterprise data security, privacy compliance, and token-cost management for AI workloads.