Lead Software Engineer - AI

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The Lead Software Engineer codes software applications based on business requirements. The Lead Software Engineer works on problems of diverse scope and complexity ranging from moderate to substantial.

The Lead Software Engineer standardizes the quality assurance procedure for software. Oversees testing and debugging and develops fixes. Researches complaints and makes necessary adjustments and/or recommendations to resolve complex software related issues. Advises executives to develop functional strategies (often segment specific) on matters of significance. Exercises independent judgment and decision making on complex issues regarding job duties and related tasks, and works under minimal supervision, Uses independent judgment requiring analysis of variable factors and determining the best course of action.

Key Responsibilities

Technical Architecture and Ownership:** Design and own the end-to-end architecture of Centerwell's AI systems, including LLM-powered clinical tools, RAG pipelines, harnesses, agent-based workflows, and intelligent automation. Make and communicate foundational technical decisions in close collaboration with the broader engineering team.

Model Development and Fine-Tuning:** Evaluate, select, and where appropriate guide the fine-tuning of foundation models. Establish model evaluation frameworks that prioritize safety, accuracy, and clinical relevance.

Clinical and Product Partnership:** Collaborate closely with product managers, designers, clinicians, and data stakeholders to understand care delivery workflows and translate them into well-scoped, high-impact AI features.

HIPAA Compliance and Responsible AI:** Ensure all AI systems are designed, deployed, and monitored in compliance with HIPAA and Humana's Responsible AI standards, including participation in AIRB and Responsible AI Council review processes where applicable.

Required Qualifications

B.S. or M.S. in Computer Science, Engineering, or a related field (or equivalent experience)

8+ years of software engineering experience, with 4+ years focused on production AI/ML systems

Deep, hands-on experience building with large language models and orchestration frameworks (LangChain, LlamaIndex, or equivalent)

Strong proficiency in Python, Typescript and modern ML tooling (Hugging Face, PyTorch, or similar)

Proven experience designing and deploying RAG pipelines and vector search systems in production

Working knowledge of model fine-tuning approaches (LoRA, RLHF, instruction tuning, or equivalent)

Experience designing and implementing custom LLM and agent evaluation frameworks, including automated eval pipelines, benchmark construction, and output quality assessment at scale

Experience designing and building multi-agent AI systems, including agent orchestration, tool use, and sandboxed execution environments

Demonstrated ownership of complex AI systems end-to-end, from architecture through production operation

Understanding of HIPAA requirements and secure handling of protected health information (PHI)

Strong written and verbal communication skills across technical and non-technical audiences

Hands-on experience with AI-assisted development tools and LLM coding harnesses (Claude Code, Pi Agent, Cursor, Codex, or similar)

Preferred Qualifications

Experience in healthcare, clinical informatics, or other highly regulated industries

Exposure to clinical NLP, medical coding, or EHR/FHIR data standards

Experience operating AI systems at scale on cloud platforms (AWS, Azure, or GCP)