Hi,

Hope you are doing good!

We have a Full-time opportunity for you as Data Scientist @ Multiple Locations Across USA

Role: Data Scientist

Locations: Bellevue, WA / Richardson, TX / Austin, TX / Houston, TX / Tempe, AZ / Phoenix, AZ / Denver, CO / Charlotte, NC / Raleigh, NC / Alpharetta, GA / Tampa, FL / Palm Beach, FL / Sunnyvale, CA / Hartford, CT / New York, NY / Bridgewater, NJ / Washington, VA

Type of Hiring: FTE

Job Description:

Client 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

• 5–8 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.

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 Data Scientist 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.

Experience with multimodal models and perception systems (e.g., vision + language).