DATAECONOMY Inc.
Generative AI Engineer (Data/ML/GenAI)
DATAECONOMY Inc., Jersey City, New Jersey, United States, 07390
Generative Ai Engineer (Data/Ml/Genai)
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 rigorevaluation, monitoring, safety/guardrails, and cost/latency optimization. Key Responsibilities
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. Build prompt stacks (system/task/tool), synthetic data pipelines, and fine-tune or LoRA adapters; apply instruction tuning/RLHF where warranted. Implement multi-agent/tool-calling workflows using AutoGen, LangGraph, CrewAI (state management, retries, tool safety, fallbacks, grounding). Stand up retrieval stacks with vector DBs (Pinecone/Faiss/Weaviate/pgvector), chunking and citation strategies, reranking, and caching; enforce traceability. Ship FastAPI services, containerize (Docker), orchestrate (Kubernetes/Cloud Run), wire CI/CD and IaC; design SLAs/SLOs for reliability and cost. Instrument evals (unit/regression/AB), add tracing and metrics (Langfuse, LangSmith, OpenTelemetry), and manage model/version registries (MLflow/W&B). Implement guardrails (prompt injection/PII/toxicity), policy filters (Bedrock Guardrails/Azure AI Content Safety/OpenAI Moderation), access controls, and compliance logging. Build/maintain data ingestion, cleansing, and labeling workflows for model/retrieval corpora; ensure schema/version governance. Optimize with batching, streaming, JSON-schema/function calling, tool-use, speculative decoding/KV caching, and token budgets. Partner with product/engineering/DS; review designs/PRs, mentor juniors, and drive best practices/playbooks. Preferred Qualifications
Deeper experience with multi-agent planning/execution, tool catalogs, and failure-mode design. Experience with pgvector/Elasticsearch/OpenSearch; comfort with relational/NoSQL/graph stores. Human-in-the-loop pipelines, golden sets, regression suites, and cost/quality dashboards. OSS contributions, publications, talks, or a strong portfolio demonstrating GenAI craftsmanship. Nice to Have
Redis/Celery, task queues, and concurrency controls for bursty LLM traffic. Experience with API gateways (e.g., MuleSoft), authN/Z, and vendor compliance reviews. Prior work in data-heavy or regulated domains (finance/health/gov) with auditable GenAI outputs. Requirements
6+ years across Data/ML/GenAI, with 12+ years designing and shipping LLM or GenAI apps to production. Strong Python and FastAPI; proven experience building secure, reliable REST services and integrations. Hands-on with OpenAI/Anthropic/Gemini/Llama families and at least two of AutoGen, LangGraph, CrewAI, LangChain, LlamaIndex, Transformers. Practical experience implementing vector search and reranking, plus offline/online evals (RAGAS, promptfoo, custom harnesses). Docker, Kubernetes (or managed equivalents), and one major cloud (AWS/Azure/GCP); CI/CD and secrets management. Familiarity with tracing/metrics tools (Langfuse, LangSmith, OpenTelemetry) and setting SLIs/SLOs. Working knowledge of data privacy, PII handling, content safety, and policy/controls for enterprise deployments. Clear technical writing and cross-functional collaboration; ability to translate business goals into architecture and milestones.
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 rigorevaluation, monitoring, safety/guardrails, and cost/latency optimization. Key Responsibilities
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. Build prompt stacks (system/task/tool), synthetic data pipelines, and fine-tune or LoRA adapters; apply instruction tuning/RLHF where warranted. Implement multi-agent/tool-calling workflows using AutoGen, LangGraph, CrewAI (state management, retries, tool safety, fallbacks, grounding). Stand up retrieval stacks with vector DBs (Pinecone/Faiss/Weaviate/pgvector), chunking and citation strategies, reranking, and caching; enforce traceability. Ship FastAPI services, containerize (Docker), orchestrate (Kubernetes/Cloud Run), wire CI/CD and IaC; design SLAs/SLOs for reliability and cost. Instrument evals (unit/regression/AB), add tracing and metrics (Langfuse, LangSmith, OpenTelemetry), and manage model/version registries (MLflow/W&B). Implement guardrails (prompt injection/PII/toxicity), policy filters (Bedrock Guardrails/Azure AI Content Safety/OpenAI Moderation), access controls, and compliance logging. Build/maintain data ingestion, cleansing, and labeling workflows for model/retrieval corpora; ensure schema/version governance. Optimize with batching, streaming, JSON-schema/function calling, tool-use, speculative decoding/KV caching, and token budgets. Partner with product/engineering/DS; review designs/PRs, mentor juniors, and drive best practices/playbooks. Preferred Qualifications
Deeper experience with multi-agent planning/execution, tool catalogs, and failure-mode design. Experience with pgvector/Elasticsearch/OpenSearch; comfort with relational/NoSQL/graph stores. Human-in-the-loop pipelines, golden sets, regression suites, and cost/quality dashboards. OSS contributions, publications, talks, or a strong portfolio demonstrating GenAI craftsmanship. Nice to Have
Redis/Celery, task queues, and concurrency controls for bursty LLM traffic. Experience with API gateways (e.g., MuleSoft), authN/Z, and vendor compliance reviews. Prior work in data-heavy or regulated domains (finance/health/gov) with auditable GenAI outputs. Requirements
6+ years across Data/ML/GenAI, with 12+ years designing and shipping LLM or GenAI apps to production. Strong Python and FastAPI; proven experience building secure, reliable REST services and integrations. Hands-on with OpenAI/Anthropic/Gemini/Llama families and at least two of AutoGen, LangGraph, CrewAI, LangChain, LlamaIndex, Transformers. Practical experience implementing vector search and reranking, plus offline/online evals (RAGAS, promptfoo, custom harnesses). Docker, Kubernetes (or managed equivalents), and one major cloud (AWS/Azure/GCP); CI/CD and secrets management. Familiarity with tracing/metrics tools (Langfuse, LangSmith, OpenTelemetry) and setting SLIs/SLOs. Working knowledge of data privacy, PII handling, content safety, and policy/controls for enterprise deployments. Clear technical writing and cross-functional collaboration; ability to translate business goals into architecture and milestones.