Senior AI Developer

Job Description:

• Senior AI Developer (Full?Stack)
• Senior, hands?on AI engineer to design, build, and productionize GenAI applications end?to?end.
• Candidates will lead the development of robust LangChain/LangGraph agentic workflows, high?quality RAG pipelines, and scalable microservices on Google Vertex AI.
• Candidates will 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 and 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 and 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/Next.js) 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 and 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 and 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 and 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 to 10 plus years software engineering experience; 3 to 5 plus 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 (Google Cloud Platform 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 and 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).

Top 3 Requirements:

• Python
• LangChain