About this Senior AI Engineer role at NeueHealth
WHO WE ARE
NeueHealth is a value-driven healthcare company grounded in the belief that all health consumers are entitled to high-quality, coordinated care. By uniquely aligning the interests of health consumers, providers, and payors, we help to make healthcare accessible and affordable to all populations across the ACA Marketplace, Medicare, and Medicaid.
NeueHealth delivers clinical care to health consumers through our owned clinics – Centrum Health and Premier Medical – as well as unique partnerships with affiliated providers across the country. We also enable providers to succeed in performance-based arrangements through a suite of technology and services scaled centrally and deployed locally. Through our value-driven, consumer-centric approach, we are committed to transforming healthcare and creating a better care experience for all.
JOB SUMMARY
The Senior AI Engineer leads the design, delivery, and production operation of complex healthcare AI products spanning traditional software enhanced by AI to AI-native, agentic workflows. This hands-on role owns prompts, agent behavior, retrieval, model optimization, evaluations, and human-review controls that make high-stakes workflows safe, reliable, observable, and governable.
Working independently and in close partnership with Product, business, clinical, operational, Data Engineering, Platform, and Security teams, the Senior AI Engineer translates real-world workflows into configuration-driven decision logic and scalable systems for operating AI at enterprise scale. They curate domain datasets, drive evaluation-based iteration, optimize models and retrieval, and help advance what is possible with AI in a highly regulated healthcare environment.
This is a work-from-home position; candidates located in TX, MN, or FL are preferred, subject to business need.
DUTIES & RESPONSIBILITIES
- Lead the end-to-end delivery of complex AI products including agentic workflows, RAG systems, and services.
- Collaborate with Product, Engineering, Data, and clinical and operational teams.
- Translate stakeholder needs into validated, access-aware AI workflows with clear system behavior, acceptance criteria, risk controls, and delivery trade-offs.
- Design AI workflow and RAG product lines, including ingestion, retrieval quality, attribution, confidence handling, agent tools, and human-review paths.
- Implement workflows with reliable AI control layers and optimize healthcare AI models across prompting, retrieval, reranking, fine-tuning, and model-selection approaches.
- Own healthcare AI evaluation and release frameworks, including adversarial testing, regression thresholds, release gates, and production telemetry.
- Provide senior technical leadership through design and code reviews, incident response, mentoring, and reusable engineering improvements.
- Deliver healthcare AI services using approved cloud capabilities and HITRUST-aligned controls for PHI/PII, security, privacy, auditability, responsible AI, and compliance.
EDUCATION, TRAINING, AND PROFESSIONAL EXPERIENCE
- Bachelor's degree in Computer Science, Computer Engineering, Information Systems, Data Science, Linguistics, or a related field; equivalent practical experience may be considered.
- Typically, 5+ years of relevant engineering experience, including ownership of complex production components or technical leadership of feature delivery.
- Hands-on experience delivering and operating production or near-production AI/ML/LLM-enabled applications.
- Strong software engineering expertise in service and data-contract design, testing, CI/CD, observability, debugging, reliability, and operational support.
- Strong proficiency in Python, Java, Scala, C#, TypeScript or a comparable modern language.
- Deep working knowledge of structured outputs, RAG, tool use, evaluations, guardrails, monitoring, and human-in-the-loop design.
- Experience with cloud services and data-intensive systems; ability to collaborate across structured-data products and unstructured-data pipelines.
- Demonstrated self-directed exploration of emerging AI capabilities through independently built prototypes, tools, experiments, or applications.
- Experience translating AI experimentation into production solutions for enterprise or regulated environments, with attention to safety, reliability, security, governance, and measurable outcomes.
PREFERRED EXPERIENCE
- Healthcare experience involving PHI/PII, clinical or operational workflows, payer/provider operations, auditability, safety review, compliance controls, or value-based care.
- Experience with cloud AI and data platforms such as Azure, AWS, GCP, Databricks, Agent Bricks, Genie, Azure AI Foundry/Azure OpenAI, Azure ML, AKS/Kubernetes, OpenTelemetry, or AI observability and evaluation tooling.
- Model benchmarking, selection, adaptation, fine-tuning, reranking, or other domain-model optimization techniques.
- Experience leading technical initiatives across teams or mentoring several engineers.
