Lead AI Software Engineer

Humana•Tampa, FL

6d•$129,300 - $177,800•Hybrid

About The Position

Humana is seeking a Lead AI Software Engineer to build the next generation of AI-powered products and platforms. This is a hands-on technical leadership role focused on designing, developing, and scaling production-grade AI solutions that deliver business impact. The role works closely with engineers, data scientists, product leaders, and architects to turn ideas into real-world applications using large language models, intelligent automation, and cloud-native architectures. The position provides mentorship and technical leadership to engineers and data scientists while remaining strongly focused on design, development, and execution in a fast-moving, enterprise-scale environment. The role is hybrid, based in Louisville, Kentucky; Nashville, Tennessee; Tampa or Fort Lauderdale, Florida; or Dallas, Texas, with onsite work expected several days per week and flexibility to work remotely.

Requirements

Bachelor's degree in Engineering, Data Science, or a related field.
8+ years of software engineering experience with demonstrated technical leadership.
Proven experience building and delivering large-scale, production-grade software platforms.
Strong backend development expertise with Python, Java, or similar languages.
Experience designing distributed systems and cloud-native applications.
Demonstrated ability to lead technical initiatives from architecture through deployment.
Experience working in agile environments with frequent delivery and iteration.

Nice To Haves

Hands-on experience building and deploying AI, machine learning, or generative AI solutions in production.
Experience with large language models (LLMs) and modern AI application architectures.
Knowledge of AI orchestration frameworks, agent-based systems, and model-serving platforms.
Experience with GCP, AWS, or Azure cloud platforms.
Familiarity with RAG, embeddings, vector databases, semantic search, OCR, and intelligent document processing.
Background in data engineering, data science, or MLOps.
Experience building APIs, microservices, and event-driven architectures.
Experience operating within complex or highly regulated enterprise environments.
Strong communication and stakeholder management skills.
Advanced degree in Engineering, Data Science, or a related field.

Responsibilities

Serve as technical lead for strategic AI initiatives, driving architecture, design decisions, and engineering excellence.
Design and build scalable, secure, cloud-native AI platforms and services.
Architect solutions using generative AI, large language models, intelligent document processing, and data-driven applications.
Lead design reviews, code reviews, and technical planning.
Establish engineering standards, reusable frameworks, and best practices for delivery at scale.
Provide technical guidance across the full software development lifecycle.
Build end-to-end AI solutions, including APIs, microservices, data pipelines, orchestration layers, and evaluation frameworks.
Develop production-grade applications integrating AI and machine learning capabilities.
Partner with platform, infrastructure, and DevOps teams to ensure solutions are scalable, reliable, observable, and cost-efficient.
Drive technical execution from concept through production deployment in an agile delivery environment.
Evaluate and adopt emerging AI technologies to accelerate innovation and business value.
Mentor and develop software engineers and data scientists through technical coaching and hands-on collaboration.
Promote best practices in software engineering, AI development, cloud architecture, and operational excellence.
Foster a culture of innovation, accountability, continuous learning, and high-quality execution.
Help teams balance rapid delivery with long-term maintainability and technical excellence.
Implement evaluation, testing, and validation frameworks for AI-powered applications.
Support monitoring, performance optimization, and incident response for AI systems in production.
Drive responsible AI practices focused on reliability, security, compliance, and risk management.
Ensure AI solutions meet enterprise standards for scalability, operational readiness, and governance.