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.