About Us:
Our purpose is to help clients exceed their financial health goals. Across the reimbursement cycle, our scalable solutions and clinical expertise help solve programmatic needs. Enabling our teams with leading technology allows analytics to guide our solutions and keeps us accountable achieving goals.
We build long-term careers by investing in YOU. We seek to create an environment that cultivates your professional development and personal growth, as we believe your success is our success.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Note: The essential duties and responsibilities below are intended to describe the general duties and responsibilities of this position and are not intended to be an exhaustive statement of duties. This position may perform all or most of the primary duties listed below. Specific tasks, responsibilities or competencies may be documented in the Team Member’s performance objectives as outlined by the Team Member’s immediate Leadership Team Member.
About the Role
We are looking for an AI Engineer with a strong focus on Large Language Models (LLMs) and Generative AI to design, build, and deploy intelligent, LLM-powered systems. You'll work on prompt engineering, RAG pipelines, agentic workflows, and fine-tuning, while building robust backend services in Java and Python — with React front-end knowledge as a plus to help ship end-to-end AI features.
Key Responsibilities
- Design, build, and deploy LLM-powered applications — chatbots, copilots, agents, RAG systems, and summarization/extraction pipelines
- Develop and optimize prompt engineering strategies (few-shot, chain-of-thought, structured output, function/tool calling) for production use cases
- Build and maintain Retrieval-Augmented Generation (RAG) pipelines — embeddings, chunking strategies, vector databases (Pinecone, Weaviate, FAISS, pgvector)
- Fine-tune and evaluate LLMs (open-source and closed-source) using techniques like LoRA/QLoRA, instruction tuning, and RLHF where applicable
- Design and implement agentic workflows using frameworks like LangChain, LangGraph, LlamaIndex, or custom orchestration
- Build backend services and APIs in Java and/or Python to serve LLM applications at scale
- Implement evaluation frameworks for LLM outputs — accuracy, hallucination rate, latency, cost, and safety metrics
- Manage prompt versioning, model routing, and caching for cost/performance optimization across multiple LLM providers (OpenAI, Anthropic, open-source models)
- Collaborate with front-end developers (or contribute directly in React) to surface LLM features in user-facing products
- Monitor deployed LLM systems for drift, degradation, and safety/guardrail violations; iterate on mitigation strategies
- Implement guardrails, content filtering, and responsible AI practices (bias mitigation, prompt injection defense, data privacy)
- Stay current with the fast-moving LLM/GenAI landscape (new models, techniques, tooling) and assess applicability to the business
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Machine Learning, or related field (or equivalent practical experience)
- Strong programming skills in Java and Python
- Hands-on experience with LLM APIs (OpenAI, Anthropic Claude, Gemini, etc.) and open-source LLMs (Llama, Mistral, etc.)
- Practical experience with prompt engineering and RAG architecture
- Experience with vector databases and embedding models
- Familiarity with LLM orchestration frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar
- Solid understanding of transformer architecture and how LLMs work under the hood
- Experience building and consuming REST/gRPC APIs for AI-serving backends
- Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes)
Preferred Qualifications
- Experience fine-tuning LLMs (LoRA, QLoRA, PEFT) and working with Hugging Face Transformers
- Experience designing multi-agent systems or tool-using/agentic AI workflows
- Familiarity with LLM evaluation tools (RAGAS, DeepEval, promptfoo, human-in-the-loop eval pipelines)
- Working knowledge of React to build or collaborate on UI components for LLM-driven features (chat interfaces, streaming responses, etc.)
- Understanding of guardrails/safety tooling (Guardrails AI, NeMo Guardrails, content moderation APIs)
- Experience with MLOps/LLMOps practices — CI/CD, model/prompt versioning, observability (LangSmith, Weights & Biases, Arize)
- Background in NLP or contributions to open-source LLM/GenAI projects
Soft Skills
- Curiosity and adaptability — comfortable navigating a fast-evolving space where best practices shift monthly
- Strong problem-solving and analytical thinking
- Ability to communicate technical AI concepts to non-technical stakeholders
- Collaborative mindset and eagerness to mentor/learn from peers
PHYSICAL DEMANDS:
Note: Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions as described. Regular eye-hand coordination and manual dexterity is required to operate office equipment. The ability to perform work at a computer terminal for 6-8 hours a day and function in an environment with constant interruptions is required. At times, Team Members are subject to sitting for prolonged periods. Infrequently, Team Member must be able to lift and move material weighing up to 20 lbs. Team Member may experience elevated levels of stress during periods of increased activity and with work entailing multiple deadlines.
A job description is only intended as a guideline and is only part of the Team Member’s function. The company has reviewed this job description to ensure that the essential functions and basic duties have been included. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.
