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Selected role

Generative AI Engineer

NeerInfo Solutions Richardson, TX

Senior Full-time

Seeking a hands-on Gen AI / Agentic AI Lead to drive the development and deployment of next-generation AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI frameworks. This role is ideal for a mid-level engineer with strong technical depth, a passion for building, and the ability to lead small teams or workstreams in a fast-paced, innovation-driven environment.

Required Qualifications
Bachelor’s degree in Computer Science, AI/ML, or related field.
7+ years of experience in software engineering or data science, with 2–3 years in Gen AI or LLM-based systems.
Strong Python programming skills and experience with ML/AI libraries (Hugging Face Transformers, LangChain, PyTorch).
Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search).
Familiarity with cloud platforms and Gen AI services (AWS, Azure, GCP).
Experience with REST API development (FastAPI, Flask) and containerization (Docker).
Solid understanding of AI governance, model safety, and prompt engineering.
This position is located in Bridgewater, NJ; Sunnyvale, CA; Austin, TX; Raleigh, NC; Richardson, TX; Tempe, AZ; Phoenix, AZ; Charlotte, NC; Houston, TX; Denver, CO; Hartford, CT; New York, NY, Palm Beach, FL; Tampa, FL or Alpharetta, GA, or is willing to relocate.
Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply.
Key Responsibilities
Design, develop, and deploy Gen AI applications using LLMs and agentic frameworks (e.g., LangGraph, AutoGen, Crew AI).
Fine-tune open-source and proprietary LLMs using techniques like LoRA, QLoRA, and PEFT.
Build and optimize RAG pipelines with hybrid retrieval, semantic chunking, and vector search.
Integrate Gen AI solutions with cloud-native services (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
Work with unstructured data (PDFs, HTML, audio, images) and multimodal models.
Implement LLMOps practices including prompt versioning, caching, observability, and cost tracking.
Evaluate model performance using tools like RAGAS, DeepEval, and FMeval.
Collaborate with product managers, data engineers, and UX teams to deliver production-ready solutions.
Mentor junior engineers and contribute to code reviews, design discussions, and best practices.
Preferred
Qualifications:
Exposure to agentic workflows and autonomous agents.
Experience with CI/CD pipelines and DevOps tools (GitHub Actions, Jenkins, Terraform).
Familiarity with front-end integration (React, Angular, TypeScript) and GraphQL APIs.
Knowledge of model interpretability, bias mitigation, and human-in-the-loop systems.

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