Job Description
Job Title
Generative AI Engineer (Data/ML/GenAI)
Jerser City, NJ
Full-time
Job Summary
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.
Domain experience:
Prior work in data-heavy or regulated domains (finance/health/gov) with auditable GenAI outputs.
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.