Generative AI Engineer (Data/ML/GenAI)

Job Title

We’re hiring a Generative AI Engineer with 6+ years across Data/ML/GenAI who can design, build, and productionize LLM-powered systems end-to-end. You’ll select and fine-tune models (OpenAI, Anthropic, Google, Meta, open-source), craft robust RAG/agentic workflows (AutoGen, LangGraph, CrewAI, LangChain/LlamaIndex), and ship secure, observable services with FastAPI, Docker, and Kubernetes. You pair strong software engineering with MLOps/LLMOps rigor—evaluation, monitoring, safety/guardrails, and cost/latency optimization.

Key Responsibilities

• Solution architecture: Own E2E design for chat/agents, structured generation, summarization/classification, and workflow automation. Choose the right model vs. non-LLM alternatives and justify trade-offs.
  
• Prompting & tuning: Build prompt stacks (system/task/tool), synthetic data pipelines, and fine-tune or LoRA adapters; apply instruction tuning/RLHF where warranted.
  
• Agentic systems: Implement multi-agent/tool-calling workflows using AutoGen, LangGraph, CrewAI (state management, retries, tool safety, fallbacks, grounding).
  
• RAG at scale: Stand up retrieval stacks with vector DBs (Pinecone/Faiss/Weaviate/pgvector), chunking and citation strategies, reranking, and caching; enforce traceability.
  
• APIs & deployment: Ship FastAPI services, containerize (Docker), orchestrate (Kubernetes/Cloud Run), wire CI/CD and IaC; design SLAs/SLOs for reliability and cost.
  
• LLMOps & observability: Instrument evals (unit/regression/AB), add tracing and metrics (Langfuse, LangSmith, OpenTelemetry), and manage model/version registries (MLflow/W&B).
  
• Safety & governance: Implement guardrails (prompt injection/PII/toxicity), policy filters (Bedrock Guardrails/Azure AI Content Safety/OpenAI Moderation), access controls, and compliance logging.
  
• Data & pipelines: Build/maintain data ingestion, cleansing, and labeling workflows for model/retrieval corpora; ensure schema/version governance.
  
• Performance & cost: Optimize with batching, streaming, JSON-schema/function calling, tool-use, speculative decoding/KV caching, and token budgets.
  
• Collaboration & mentoring: Partner with product/engineering/DS; review designs/PRs, mentor juniors, and drive best practices/playbooks.
  

Preferred Qualifications

• Agent ecosystems: Deeper experience with multi-agent planning/execution, tool catalogs, and failure-mode design.
  
• Search & data stores: Experience with pgvector/Elasticsearch/OpenSearch; comfort with relational/NoSQL/graph stores.
  
• Advanced evals: Human-in-the-loop pipelines, golden sets, regression suites, and cost/quality dashboards.
  
• Open-source & thought leadership: OSS contributions, publications, talks, or a strong portfolio demonstrating GenAI craftsmanship.
  

Nice to Have

• Eventing & rate limiting: Redis/Celery, task queues, and concurrency controls for bursty LLM traffic.
  
• Enterprise integrations: Experience with API gateways (e.g., MuleSoft), authN/Z, and vendor compliance reviews.
  

Requirements

• Experience: 6+ years across Data/ML/GenAI, with 1–2+ years designing and shipping LLM or GenAI apps to production.
  
• Languages & APIs: Strong Python and FastAPI; proven experience building secure, reliable REST services and integrations.
  
• Models & frameworks: Hands-on with OpenAI/Anthropic/Gemini/Llama families and at least two of: AutoGen, LangGraph, CrewAI, LangChain, LlamaIndex, Transformers.
  
• RAG & retrieval: Practical experience implementing vector search and reranking, plus offline/online evals (e.g., RAGAS, promptfoo, custom harnesses).
  
• Cloud & DevOps: Docker, Kubernetes (or managed equivalents), and one major cloud (AWS/Azure/GCP); CI/CD and secrets management.
  
• Observability: Familiarity with tracing/metrics tools (e.g., Langfuse, LangSmith, OpenTelemetry) and setting SLIs/SLOs.
  
• Security & governance: Working knowledge of data privacy, PII handling, content safety, and policy/controls for enterprise deployments.
  
• Communication: Clear technical writing and cross-functional collaboration; ability to translate business goals into architecture and milestones.