Senior AI Developer (Full Stack)

Job Title: Senior AI Developer (Full Stack)

Senior hands on AI engineer to design build and productionize GenAI applications end to end. You ll lead the development of robust LangChain/LangGraph agentic workflows high quality RAG pipelines and scalable microservices on Google Vertex AI. You ll own system design implementation MLOps observability and governance partnering closely with product data security and platform teams to deliver reliable secure and cost efficient AI products.

Key Responsibilities

• Architecture & Orchestration
• Design multi step agentic workflows with LangGraph (state machines tools retries timeouts) and LangChain (chains tools memory).
• Build guardrails (input/output filtering red teaming hooks) and observability (tracing telemetry logging prompt/version tracking).
• RAG Pipelines
• Own ingestion pipelines: chunking embeddings document normalization metadata and vector DB indexing (e.g. Pinecone Weaviate Milvus FAISS).
• Implement retrieval strategies: hybrid (BM25 dense) multi vector reranking query planning LangGraph retrieval sub graphs caching.
• Build domain specific adapters (schema ontology alignment) and grounding with structured tools/knowledge bases.
• Vertex AI & Platform Engineering
• Productionize services on Google Vertex AI (Models Endpoints Workbench Pipelines Vector Search Feature Store).
• Containerize with Docker orchestrate with Kubernetes/GKE and automate with CI/CD (GitHub Actions/Cloud Build).
• Full Stack Delivery
• Build user facing apps (React/) and backends (Python/FastAPI Node/Express) including authentication/authorization and rate limiting.
• Develop tooling/services (e.g. document loaders evaluators red teaming flows prompt versioning synthetic data pipelines).
• Evaluation & Reliability
• Define and automate GenAI evaluation: relevance faithfulness hallucination rate answer exactness latency cost.
• Use techniques like RAGAS G Eval rubric based human in the loop pairwise comparisons A/B tests and production feedback loops.
• Security Governance & Cost
• Implement data privacy controls (PII detection masking) policy enforcement prompt hardening and audit logging.
• Optimize latency and TCO (embedding/model selection batching caching streaming adaptive routing quantization where applicable).
• Mentorship & Standards
• Establish best practices for prompt patterns orchestration testing (unit & scenario) and model lifecycle management.
• Mentor engineers; collaborate with product/design to scope features and deliver business impact.

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 plus React/ 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).