DATAECONOMY Inc.
Generative AI Engineer (Data/ML/GenAI)
DATAECONOMY Inc., Jersey City, New Jersey, United States, 07390
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.
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.