Lead Generative AI Engineer

Job Description :

Required Qualifications :

- 5+ years building cloud applications with at least good years on GCP.

- Delivering GenAI solutions in production (chat, copilots, summarization, extraction, or agentic workflows).

- Strong with Vertex AI : training/tuning (custom or LoRA/adapter), endpoints, batch/online prediction, embeddings and Vector Search, Workbench/Notebooks.

- Proficiency in Python (FastAPI/Flask), or Node.js; comfortable with REST/gRPC and event-driven designs.

- IaC with Terraform (modules, workspaces, state management) and secured CI/CD (Cloud Build/Cloud Deploy or GitHub Actions).

- Containers and orchestration : Docker and GKE or Cloud Run; understanding of service meshes, autoscaling, and rollout strategies.

- Data stack : BigQuery, Cloud Storage, Dataflow/Beam basics; feature/embedding pipelines.

- Security/compliance fundamentals : IAM, VPC Service Controls, CMEK/KMS, artifact signing, network policies, and secrets.

- Solid understanding of RAG, vector databases, prompt engineering, evaluation metrics, and guardrails.

Preferred Qualifications :

- Experience with multi-agent/agentic frameworks and tool-calling; LangChain/LangGraph or equivalent.

- Familiarity with governance and safety tooling (content moderation, toxicity filters, jailbreak/PII detection).

- Observability : OpenTelemetry, SLO design, chaos/resiliency testing.

- MLOps tools : MLflow/Vertex ML Metadata, experiment tracking, model registry.

- Performance tuning for LLM inference: batching, quantization, caching, model distillation, cost controls.

- Additional cloud exposure (AWS Bedrock or Azure OpenAI) and hybrid patterns.

- Prior work in regulated environments (HIPAA, SOC 2, FedRAMP) a plus.

Nice-to-Have Tech :

- Frameworks : LangChain/LangGraph, Ray, Beam.

- Datastores : Bigtable, Firestore, Redis, Elasticsearch, AlloyDB.

- Frontend integration for AI UX (React) and conversation memory patterns.

- Testing/eval : prompt/unit tests, golden datasets, offline/online evals, AB testing.

What Youll Work On (Examples) :

- A Vertex AIpowered RAG service using BigQuery + Vertex Vector Search, served on Cloud Run with Terraform-managed infra and Cloud Build CI/CD.

- An agentic workflow that orchestrates tools (REST/GraphQL/gRPC) to complete multi-step tasks with audit trails and human-in-the-loop review.

- A secured inference platform with per-tenant isolation, CMEK, egress controls, and cost dashboards.