Must Have Technical/Functional Skills
Programming: Expert-level proficiency in Python (plus Go, Java, or C++ as needed).
AI Frameworks: Hands-on experience with LangChain, LlamaIndex, LangGraph, CrewAI, or AutoGen.
Data & Retrieval: Vector databases, hybrid search, document chunking, and knowledge graphs (e.g., Neo4j).
Cloud & MLOps: Containerization with Docker and Kubernetes, CI/CD pipelines, and cloud platforms like AWS, GCP, or Azure.
Observability: Production evaluation and tracing tools for monitoring model drift and output quality.
Roles & Responsibilities
- End-to-End Delivery: Own the full lifecycle of GenAI applications from initial design and testing to cloud deployment and monitoring. [1, 2]
- Agentic Workflows: Build autonomous, goal-driven AI agents that can plan, reason, and execute multi-step workflows using tool-calling and APIs. [1]
- RAG & Knowledge Integration: Implement advanced retrieval systems, vector databases, and Graph RAG to ground AI responses in enterprise data and prevent hallucinations. [1, 2]
- System Optimization: Tune latency, throughput, cost-efficiency, and hardware utilization for large-scale LLM inference. [1]
- Governance & Safety: Enforce responsible AI standards, including data privacy compliance, automated testing, evaluation tracing, and security guardrails. [1, 2]
- Leadership: Mentor junior engineers, drive architectural best practices, and bridge the gap between technical teams and business stakeholders.
Salary Range: $80,000 to $125,000 per year