Lead Software Engineer - AI

Humana•Tampa, FL

3d•$155,200 - $213,400•Remote

About The Position

The Lead Software Engineer builds software applications based on business requirements and works on problems of diverse scope and complexity, from moderate to substantial. The role standardizes software quality assurance procedures, oversees testing and debugging, develops fixes, and investigates complaints to resolve complex software issues. It also advises executives on significant functional strategies and exercises independent judgment in making decisions on complex work, with minimal supervision. For CenterWell’s AI systems, this role owns end-to-end technical architecture, including LLM-powered clinical tools, RAG pipelines, evaluation harnesses, agent-based workflows, and intelligent automation. The engineer evaluates foundation models and may guide fine-tuning, while establishing evaluation frameworks that prioritize safety, accuracy, and clinical relevance. The position partners with product managers, designers, clinicians, and data stakeholders to translate care-delivery workflows into high-impact AI features. The role ensures AI systems comply with HIPAA and Humana’s Responsible AI standards, and participates in AIRB and Responsible AI Council review processes where applicable. This is a remote position, with occasional travel to Humana offices for training or meetings. The candidate must live close to one of the designated IT locations. Work-from-home employees must have a dedicated space to protect member PHI and an internet connection with at least 25 Mbps download and 10 Mbps upload.

Requirements

B.S. or M.S. in Computer Science, Engineering, or a related field, or equivalent experience.
8+ years of software engineering experience, including 4+ years focused on production AI/ML systems.
Deep, hands-on experience building with large language models and orchestration frameworks such as LangChain, LlamaIndex, or equivalent.
Strong proficiency in Python, TypeScript, and modern ML tooling such as Hugging Face or PyTorch.
Proven experience designing and deploying production RAG pipelines and vector search systems.
Working knowledge of model fine-tuning approaches such as LoRA, RLHF, or instruction tuning.
Experience designing and implementing custom LLM and agent evaluation frameworks, including automated evaluation 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 end-to-end ownership of complex AI systems, from architecture through production operation.
Understanding of HIPAA requirements and secure handling of protected health information (PHI).
Strong written and verbal communication skills with technical and non-technical audiences.
Hands-on experience with AI-assisted development tools and LLM coding harnesses such as Claude Code, Pi Agent, Cursor, or Codex.
Must live close to one of the designated IT locations: NYC, Louisville, Tampa, Fort Lauderdale, Dallas/Frisco, Boston, Washington, D.C., Atlanta, Nashville, Chicago, or Charlotte.
For home or hybrid home/office work, must have internet service of at least 25 Mbps download and 10 Mbps upload and a dedicated workspace without ongoing interruptions.

Nice To Haves

Experience in healthcare, clinical informatics, or another highly regulated industry.
Exposure to clinical NLP, medical coding, or EHR/FHIR data standards.
Experience operating AI systems at scale on AWS, Azure, or GCP.

Responsibilities

Design and own the end-to-end architecture of CenterWell AI systems, including LLM-powered clinical tools, RAG pipelines, harnesses, agent-based workflows, and intelligent automation.
Make and communicate foundational technical decisions in collaboration with the broader engineering team.
Evaluate foundation models and guide fine-tuning where appropriate.
Establish model evaluation frameworks that prioritize safety, accuracy, and clinical relevance.
Partner with product managers, designers, clinicians, and data stakeholders to understand care-delivery workflows and define high-impact AI features.
Ensure AI systems are designed, deployed, and monitored in compliance with HIPAA and Humana Responsible AI standards.
Participate in AIRB and Responsible AI Council review processes where applicable.
Standardize software quality assurance procedures; oversee testing and debugging and develop fixes.
Investigate software-related complaints and make adjustments or recommendations to resolve complex issues.
Advise executives on significant functional strategies.