• AI Engineer

    Insight Global (Hunters Mill, NC)


    Job Description

    • Build agentic AI workflows using LangGraph

    • Develop production RAG systems

    • Create AI-powered tools and APIs integrated with enterprise systems

    • Collaborate with senior engineers on scalable AI solutions

     

    Contribute to data pipeline development and API deployment

     

    We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

    Skills and Requirements

    • 3–5 years software development experience

    • 1–2 years working with LLMs or Gen AI applications

    • Strong Python skills for production applications

    • LangChain / LangGraph or similar agentic AI frameworks

    • RAG system development

    • Embeddings and vector databases (Pinecone, FAISS, Weaviate, ChromaDB)

    • SQL proficiency

    • REST API development (FastAPI, Flask)

    • Basic Linux/Unix command-line skills

    • AWS experience (S3, EC2, Lambda)

    Strong English communication skills • Prompt engineering

    • LLM inference optimization (memory, latency, throughput)

    • Experience with multiple LLM providers (OpenAI, Anthropic, Hugging Face)

    • LLM observability tools (LangSmith, Weights & Biases)

    • Data modeling and complex SQL queries

    • Docker and containerization

    • Cost optimization for LLM applications

    • Streaming responses / real-time AI

    • Model evaluation and output validation