AI​/GenAI Engineer

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Role:
AI / GenAI Engineer (Multiple Openings)

Location:

Jersey City, NJ / Atlanta, GA / Tampa, FL / Raleigh, NC / Ohio City, OH

Job Type:
Full Time

We are looking for an AI / GenAI Engineer responsible for designing, building, and deploying production‑grade Generative AI applications and agentic systems. This includes everything from prompt engineering and RAG pipelines to multi‑agent orchestration, fine‑tuning, and integrating LLMs into scalable backend services. Your primary responsibility will be to architect and develop these AI‑powered solutions, and to coordinate with the rest of the team working on different layers of the application, data, and infrastructure stack.

Thus, a commitment to collaborative problem solving, robust system design, evaluation‑driven development, and responsible AI practices is essential.

Need Experience range in between 2-7 Years

• Strong proficiency in Python with hands‑on experience building LLM‑powered applications using frameworks such as Lang Chain, Lang Graph, Llama Index, CrewAI, or Auto Gen.
• Deep understanding of LLM fundamentals including prompt engineering, tokenization, embeddings, context windows, function/tool calling, structured outputs, and techniques like RAG (Retrieval‑Augmented Generation), fine‑tuning (LoRA / QLoRA / PEFT), and evaluation (RAGAS, LLM‑as‑a‑judge).
• Hands‑on experience working with foundation models from providers such as OpenAI, Anthropic (Claude), Google (Gemini), Meta (Llama), or Mistral, along with vector databases (Pinecone, Weaviate, Chroma, FAISS, Milvus) and orchestration of multi‑agent and tool‑using systems.
• Strong backend development experience using FastAPI / Django REST Framework, exposure to Cloud Technologies (AWS Bedrock / Azure OpenAI / Google Cloud Platform Vertex AI), containerization (Docker, Kubernetes), and familiarity with SQL and No
  
  SQL databases (Postgre
  
  SQL, Mongo
  
  DB). Knowledge of MLOps/LLMOps tools (MLflow, Lang Smith, Langfuse, Weights & Biases) and responsible AI practices (guardrails, PII handling, hallucination mitigation) is a strong plus.

• Work closely with Team Leader, product managers, data scientists, and other team members to translate business requirements into GenAI‑powered features and agentic workflows.
• Get involved in the entire life‑cycle including use‑case discovery, prototyping, prompt and pipeline design, evaluation, deployment, and monitoring of LLM applications on the assigned delivery lines.
• Do hands‑on coding, build robust RAG and agent pipelines, design clean APIs, and implement rigorous evaluation and observability for LLM systems.
• Focus on clean, high‑quality, well‑documented code, established design patterns, and engineering best practices for latency, cost, safety, and reliability of GenAI systems.
• Continuously learn emerging models, frameworks, and techniques in the fast‑moving GenAI space, implement them in real‑world scenarios, and share learnings with other team members.