Lead AI/ML Engineer

Responsibilities

• Architect, build, and deploy RAG pipelines, including chunking, embeddings, vector stores, retrieval, ranking, grounding, and evaluation.
• Design and implement Graph RAG solutions leveraging knowledge graphs for multi‑hop reasoning and structured retrieval.
• Build robust, scalable ML/LLM services using Python (and Java where applicable) with well‑designed APIs and microservices.
• Develop data processing pipelines for ingestion, transformation, metadata extraction, and indexing.
• Implement observability, monitoring, evaluation harnesses, automated testing, and CI/CD for GenAI services.
• Optimize retrieval quality, response accuracy, latency, and cost across model + retrieval layers.
• Apply responsible AI, security, and governance practices for LLM systems (e.g., content filtering, guardrails, model monitoring).
• Collaborate with product, data engineering, and cloud platform teams to translate business problems into robust AI solutions.
• Produce clear documentation, design specs, and operational runbooks for all delivered components.

• 3+ years of experience as an ML Engineer, AI Engineer, or similar role.
• Hands‑on experience building GenAI applications and RAG systems end‑to‑end.
• Strong proficiency in Python for ML/LLM development.
• Experience with vector databases (e.g., pgvector, Pinecone, Weaviate, FAISS) and embedding models.
• Knowledge of LLM frameworks (LangChain, LlamaIndex, Transformers, etc.).
• Strong understanding of cloud environments (AWS/Azure) and containerized deployments.
• Solid software engineering foundations - APIs, microservices, version control, testing, CI/CD.
• Experience with data pipelines, ETL/ELT, and processing unstructured data.
• Ability to evaluate retrieval quality, implement ranking strategies, and build evaluation datasets.
• Excellent communication skills and ability to work in cross‑functional teams.

• Graph RAG experience using knowledge graphs, graph databases, or graph‑based retrieval.
• Experience with Java for backend services, data processing, or connector development.
• Familiarity with MLOps/LLMOps tooling and practices.
• Experience integrating AI outputs into metadata/catalog systems or workflows.
• Experience with prompt engineering, guardrailing, and LLM safety controls.