Description

Required Qualifications

7–10+ years software engineering experience; 3–5+ years applied ML/GenAI building production systems.
Expert with LangChain and LangGraph (tools, agents, state graphs, retries, sub‑graphs, observability).
Hands‑on with Vertex AI (Foundational models, Endpoints, Pipelines, Vector Search, Model Garden; IAM & service architectures).
Strong RAG practitioner (chunking strategies, embeddings, hybrid retrieval, rerankers like Cohere/Rerank or bge‑rerank, evaluation).
Deep experience with vector databases (Pinecone, Weaviate, Milvus, FAISS) and embedding models (OpenAI, Vertex, Cohere, bge‑large).
Production backends in Python (FastAPI) or Node.js, plus React/Next.js front‑end experience.
Solid cloud experience (GCP preferred; AWS/Azure a plus), Docker/Kubernetes, and CI/CD.
Strong understanding of GenAI evaluation (RAGAS, G‑Eval, rubric scoring), observability (LangSmith/LlamaIndex observability/OpenTelemetry), and prompt/version management.
Knowledge of security & governance: PII handling, isolation, data residency, prompt injection defenses, secret management.
Excellent communication; proven track record turning ambiguous problem statements into shipped products.

Nice to Have

Knowledge graphs (RDF/OWL), retrieval planning, and toolformer/agent patterns.
LLM serving and routing (DG/mixture‑of‑experts, function/tool calling, Guardrails, Instructor schemas, Pydantic).
LlamaIndex experience; structured RAG (SQL/Graph RAG); function/tool calling integrations (Databases, SaaS)

Key Skills

Education

Bachelor's degree