Generative AI Engineer (Data/ML/GenAI)

Overview

About Us

About DATAECONOMY: We are a fast-growing data & analytics company headquartered in Dublin with offices in Dublin, OH, Providence, RI, and an advanced technology center in Hyderabad, India. We are clearly differentiated in the data & analytics space via our suite of solutions, accelerators, frameworks, and thought leadership. 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.

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. Qualifications

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.

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