GenAI Engineer

Location

Charlotte, United States | Posted on 01/21/2026

We are looking for a skilled AI Engineer with proven experience developing and deploying large language models (LLMs) and generative AI systems. In this role, you’ll be responsible for designing, fine‑tuning, and operationalizing models from leading providers such as OpenAI, Llama, Gemini, and Claude, along with leveraging open‑source models from platforms like Hugging Face. You’ll also build robust multi‑step workflows and intelligent agents using frameworks such as Microsoft Auto Gen, Lang Graph, and CrewAI.

This position requires strong technical expertise in generative AI, advanced software engineering abilities, fluency in Python (including FastAPI), and a solid understanding of MLOps/LLMOps principles.

• LLM Solution Design & Implementation

  Architect, develop, and implement LLM‑powered and generative AI solutions utilizing both proprietary and open‑source technologies (e.g., GPT‑4, Llama 3, Gemini, Claude). Customize and fine‑tune models for tasks such as chatbots, summarization, and content classification, evaluating the suitability of LLMs for various business needs.

  Craft, refine, and test model prompts to achieve targeted outputs. Fine‑tune pre‑trained LLMs using customized data and apply advanced techniques such as instruction tuning or reinforcement learning with human feedback as required.

• Agentic Frameworks & Workflow Automation

  Build and maintain stateful, multi‑agent workflows and autonomous AI agents using frameworks like Microsoft Auto Gen, Lang Graph, Lang Chain, Llama Index, and CrewAI. Develop custom tools that enable seamless API integration and task orchestration.

• Retrieval‑Augmented Generation (RAG)

  Design and deploy RAG pipelines by integrating vector databases (such as Pinecone, Faiss, or Weaviate) for efficient knowledge retrieval. Utilize tools like RAGAS to ensure high‑quality, traceable response generation.

• LLM API Integration & Deployment

  Serve LLMs via FastAPI‑based endpoints and manage their deployment using Docker containers and orchestration tools like Kubernetes and cloud functions. Implement robust CI/CD pipelines and focus on scalable, reliable, and cost‑efficient production environments.

  Construct data pipelines for ingestion, preprocessing, and controlled versioning of training datasets. Set up automated evaluation systems, including A/B tests and human‑in‑the‑loop feedback, to drive rapid iteration and improvement.

• Partner & Stakeholder Collaboration

  Partner with data scientists, software engineers, and product teams to scope and integrate generative AI initiatives. Communicate complex ideas effectively to both technical and non‑technical stakeholders.

• Monitoring, LLMOps, & Ethics

  Deploy rigorous monitoring and observability tools to track LLM usage, performance, cost, and hallucination rates. Enforce LLMOps best practices in model management, reproducibility, explainability, and compliance with privacy and security standards.

• Continuous Learning & Thought Leadership

  Stay abreast of the latest developments in AI/LLMs and open‑source innovations. Contribute to internal knowledge sharing, champion new approaches, and represent the organization at industry or academic events.

• Open‑Source & Community

  Participation in open‑source AI/ML projects, or a strong Git Hub profile showcasing relevant contributions or publications.

• Multi‑Agent Systems

  Hands‑on experience with advanced agentic frameworks or autonomous agent system design.

• Data Governance & Compliance

  Knowledge of data governance, security protocols, and compliance standards.

• Search & Databases

  Deep expertise in vector similarity search, indexing, and familiarity document stores such as Mongo
  
  DB, Postgre
  
  SQL, and graph databases.

• Cloud‑Native AI Services

  Experience with cloud‑native AI services like Azure ML, Cognitive Search, or equivalent platforms for scalable generative AI deployment.

• Experience:

  At least 3 years in machine learning engineering, with 1–2 years focused on building and deploying generative AI or LLM‑based applications.

• Technical
  
  Skills:
  
  

  Proficiency in Python and FastAPI, and experience…