USA_Engineer

Pay Rate Range

$48.48- 50.00/hr.

Role Title

Generative AI Engineer

Job Description

"Design and develop enterprise-grade Generative AI and LLM applications.

Build Retrieval-Augmented Generation (RAG) solutions using enterprise documents and structured/unstructured data.

Develop AI agents and multi-step agentic workflows, including tool/function calling.

Integrate LLMs through platforms/APIs such as Azure OpenAI, OpenAI, or Hugging Face.

Implement document ingestion, chunking, embedding generation, metadata filtering, semantic/hybrid search, and reranking.

Develop backend services and REST APIs using Python and FastAPI.

Apply prompt engineering techniques including structured output, few-shot prompting, context management, and guardrails.

Evaluate LLM applications for accuracy, groundedness, relevance, latency, and cost.

Implement observability and monitoring for production GenAI applications.

Work with engineering, data, product, security, and business teams to translate business requirements into GenAI solutions.

Follow responsible AI, security, privacy, and data-governance practices."

Skill Required

We are looking for a Generative AI Engineer with 3–4 years of software/ML engineering experience and strong hands-on exposure to LLMs, RAG, AI agents, embeddings, and production-grade GenAI applications.

Experience Required:5-8 Years

Essential Skills

We are looking for a Generative AI Engineer with 34 years of software/ML engineering experience and strong hands-on exposure to LLMs| RAG| AI agents| embeddings| and production-grade GenAI applications.The ideal candidate should be able to take a GenAI use case from prototype to production| including data preparation| prompt design| retrieval| model integration| evaluation| deployment| monitoring| and optimization.Key ResponsibilitiesDesign and develop enterprise-grade Generative AI and LLM applications.Build Retrieval-Augmented Generation (RAG) solutions using enterprise documents and structured/unstructured data.Develop AI agents and multi-step agentic workflows| including tool/function calling.Integrate LLMs through platforms/APIs such as Azure OpenAI| OpenAI| or Hugging Face.Implement document ingestion| chunking| embedding generation| metadata filtering| semantic/hybrid search| and reranking.Develop backend services and REST APIs using Python and FastAPI.Apply prompt engineering techniques including structured output| few-shot prompting| context management| and guardrails.Evaluate LLM applications for accuracy| groundedness| relevance| latency| and cost.Implement observability and monitoring for production GenAI applications.Work with engineering| data| product| security| and business teams to translate business requirements into GenAI solutions.Follow responsible AI| security| privacy| and data-governance practices.

Skills

Generative AI

Skills

Category

Name

Required

Importance

Experience

SkillCategoryTest1_MN

Generative AI

Yes

1

>7 years