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.
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,…