AI/LLM Engineer

AI/LLM Engineer – 18 month W2 contract, hybrid 3 days onsite / 2 days remote. Charlotte, NC. Local candidates only. $55–$65/hour. Job Description – AI/LLM Engineer (RAG & Agentic Systems) We are seeking a highly motivated AI/LLM Engineer to join a growing team focused on building next‑generation generative AI solutions within the banking and financial services domain. This role will focus on designing and productionizing Retrieval‑Augmented Generation (RAG) pipelines, agentic AI systems, and LLM-powered applications that interact with financial and structured enterprise data. Key Responsibilities Design, extend, and optimize RAG pipelines, retrieval strategies, embedding workflows, and semantic search capabilities. Build and productionize agentic AI architectures that orchestrate across: RAG workflows Structured SQL/data retrieval External APIs and downstream actions (e.g., report/PPT generation) Fine‑tune and evaluate LLMs using model adaptation techniques, prompt engineering, and inference optimization. Implement model safety mechanisms, guardrails, hallucination mitigation, and response validation strategies. Develop and maintain APIs, endpoints, and tooling for model serving, observability, monitoring, and versioning. Partner with SQL/data engineering teams to securely integrate structured enterprise data into LLM workflows. Design reusable retrieval templates and interfaces for enterprise‑scale AI applications. Implement testing frameworks and monitoring for: Latency Accuracy Hallucination rates Cost efficiency Retrieval quality Participate in architecture and vendor‑selection discussions focused on scalability, performance, and cost optimization. Required Skills Hands‑on experience building production‑grade RAG pipelines and agentic AI systems. Strong experience with LLM fine‑tuning, model adaptation, or custom inference workflows. Deep understanding of at least one LLM orchestration framework such as: LangChain LlamaIndex Similar orchestration frameworks Strong Python development and API engineering experience. Ability to clearly explain: System architecture decisions Tool/framework selection Dataset preparation Evaluation methodologies Deployment and productionization strategies Experience working on AI/ML projects such as: Chatbots Financial AI applications Intelligent document/query systems Nice‑to‑Have Skills Experience integrating LLMs with relational or structured databases. Familiarity with vector databases and embedding stores such as: Pinecone Milvus Weaviate Oracle vector/embedding capabilities Knowledge of: Prompt engineering Retrieval augmentation LLM safety Hallucination mitigation Guardrail implementation Experience with cloud infrastructure, model hosting, and monitoring in distributed environments. Exposure to Kubernetes, serverless architectures, and AI infrastructure cost optimization. Java experience is a plus for interoperability with enterprise systems. Preferred Background Banking, financial services, fintech, or enterprise AI environments. Strong academic background in Computer Science, AI/ML, Data Science, or related fields. Candidates with standout internships, research, or hands‑on AI projects are highly encouraged to apply. #J-18808-Ljbffr