Lead Applied AI Software Engineer ( AI)

Humana; Frisco (TX); Hybrid; USD 170,800 - 234,800; Full time

Benefits offered by this job

Hybrid work arrangement

Bonus incentive plan

Job summary

Humana’s Enterprise AI organization seeks a Lead Applied AI Engineer to architect and deliver advanced AI systems, including Generative AI capabilities and agents integrated into secure healthcare platforms serving millions of member interactions.

This highly technical role defines standards for AI deployment, emphasizes reliability with observability, and collaborates with platform teams to support demanding workloads while upholding HIPAA and regulatory guidelines.

Qualifications

• Over 7 years of experience in software engineering with a strong focus on applied AI/ML.
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field or equivalent practical AI leadership experience.
• Demonstrated deep expertise designing and deploying production-grade generative AI systems with RAG architectures and multi-model orchestration.
• Experience coordinating frontend, backend, data, and infrastructure teams across projects.
• Strong Python, React, FastAPI skills and knowledge of vector databases, embeddings, and LLM APIs.
• Experience establishing organization-wide best practices for prompt engineering with evaluation and observability.
• Familiarity with responsible AI, governance, and regulatory requirements in healthcare environments.

Responsibilities

• Architect end-to-end AI systems with advanced RAG pipelines, multi-model integrations, and agent orchestration.
• Define prompt engineering standards, evaluation metrics, and performance optimization strategies.
• Lead deployment of AI systems with strong observability, logging, monitoring, and incident response in production.
• Design scalable data ingestion and retrieval architectures using vector databases and hybrid search.
• Establish evaluation frameworks and drive continuous improvement via A/B tests and telemetry.
• Collaborate with platform teams to ensure GPU, networking, and storage readiness for AI workloads.
• Mentor engineers and promote responsible AI practices across the organization.
• Ensure compliance with HIPAA, FDA guidelines and healthcare regulations in design and implementation.

Skills

Applied AI/ML

Distributed systems

Full-stack architectures

Leadership

Python

React

FastAPI

Vector databases

Embedding models

LLM APIs

Agent orchestration

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Equivalent practical AI leadership experience

Tools

Python

React

FastAPI

Vector databases

Embedding models

LLM APIs

AI orchestration frameworks

Job description

Become a part of our caring community

The Enterprise AI organization at Humana is a pioneering force, driving AI development across our Insurance and CenterWell business segments. By collaborating with world‑leading experts, we are at the forefront of delivering cutting‑edge AI technologies for improving care quality and experience of millions of consumers. Our goal is to create safe AI solutions that will revolutionize and improve healthcare experience and outcomes for our customers.

We are actively seeking top talent to join us in shaping the future of healthcare through AI excellence. Join our rapidly expanding team of dedicated product managers, data scientists, engineers, policy experts, and business leaders as we work together to build impactful and beneficial AI systems.

At Humana, applied artificial intelligence is central to driving intelligent automation that reduces administrative burden, enabling personalization that delivers tailored member experiences, and optimizing operational efficiency across the complex healthcare ecosystem. We are seeking an accomplished Lead Applied AI Engineer. This engineer will architect and deliver advanced AI systems. These systems will seamlessly integrate Generative AI capabilities and agents. They will integrate into secure, scalable healthcare platforms. These platforms handle millions of member interactions. They maintain the highest standards of data privacy and system reliability.

This highly technical and influential role defines technical standards for AI deployment across the organization. It ensures that AI systems are reliable through rigorous testing and monitoring, measurable through comprehensive metrics and evaluation frameworks. Additionally, it ensures compliance with healthcare regulations and ethical guidelines, and strategic alignment with enterprise architecture and business strategy. The Lead Applied AI Engineer will operate at the critical intersection of AI innovation and responsible healthcare technology, balancing the rapid pace of AI advancement with the careful, deliberate approach required in healthcare environments.

Key Responsibilities

• Architect comprehensive end‑to‑end AI systems, including sophisticated RAG pipelines with multi‑stage retrieval and re‑ranking. These pipelines are designed with appropriate modularity, extensibility, and operational characteristics to support evolving business requirements. Additionally, the systems include complex agent orchestration systems that coordinate multiple specialized agents. Furthermore, they feature multi‑model integrations that leverage different AI models for their respective strengths.
• Define rigorous standards for prompt engineering, including templates, versioning, and testing methodologies. Establish comprehensive evaluation metrics that capture both technical performance and business value. Develop performance optimization strategies, including model selection criteria, caching approaches, and resource utilization patterns, that teams across the organization can adopt to accelerate AI delivery.
• Lead deployment of AI systems into production environments with strong observability. This includes detailed logging and tracing. Comprehensive reliability is also crucial, featuring graceful degradation and circuit breakers. Monitoring is essential, with real‑time dashboards and automated alerting. Additionally, robust incident response procedures are necessary. The goal is to ensure AI services meet stringent service level objectives required for healthcare applications.
• Design scalable data ingestion architectures that can process diverse data sources, including structured databases, unstructured documents, and real‑time streams. Implement efficient retrieval architectures using vector databases and hybrid search approaches. Develop data preprocessing pipelines that clean and enrich data for AI consumption. Establish data quality monitoring to ensure AI systems operate on high‑quality inputs.
• Drive quantitative evaluation and continuous improvement of AI systems through establishment of evaluation frameworks. Implement A/B testing capabilities, analyze user feedback and system telemetry, and systematically iterate on prompts, retrieval strategies, and model configurations. This progressive iteration improves system performance and user satisfaction over time.
• Collaborate strategically with platform teams to ensure infrastructure readiness for demanding AI workloads. This includes ensuring GPU availability, appropriate networking configurations, and optimized data storage. Define requirements for AI‑specific platform capabilities, such as model serving infrastructure and feature stores. Partner on integration of AI systems with enterprise services.
• Mentor engineers at various levels through technical guidance, code reviews, architecture discussions, and career development support. Elevate AI engineering best practices across the organization through creation of documentation, delivery of training sessions, and establishment of communities of practice. Foster a culture of responsible AI development that prioritizes ethics, transparency, and user benefit.
• Ensure AI solutions rigorously meet healthcare compliance requirements through comprehensive documentation of system behavior and decision logic. Implementation of ethical standards prevents algorithmic bias and ensures fairness across different populations. Adherence to regulatory frameworks, including HIPAA, FDA guidance for clinical decision support, and emerging AI‑specific regulations, is also crucial.

Required Qualifications

• Over 7 years of experience in software engineering with a strong focus on applied AI/ML. This experience includes building and operating distributed systems at scale, as well as developing full‑stack architectures that combine backend services with modern web applications. Leadership of significant projects has delivered measurable business impact through AI capabilities.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field. Alternatively, a candidate can demonstrate equivalent practical experience through significant technical leadership in AI projects, recognized contributions to the AI engineering community, or progressive career advancement into increasingly responsible AI technical leadership roles.
• Demonstrated deep expertise designing and deploying production‑grade generative AI systems. These systems included sophisticated RAG architectures with multi‑hop retrieval and reasoning, as well as agent orchestration frameworks that coordinate multiple AI agents with tool use and memory. Additionally, they featured multi‑model systems that combine different AI capabilities, and conversational AI systems that maintain context and handle complex dialogues.
• Complex AI initiatives across multiple teams with different specializations. This involves translating high‑level business objectives into concrete AI system designs and technical roadmaps. Additionally, coordinating implementation across frontend, backend, data, and infrastructure teams. Finally, driving projects from conception through production deployment and ongoing optimization.
• Strong technical proficiency in Python, including advanced language features and design patterns. Extensive experience with modern web application frameworks, such as React and FastAPI, and familiarity with best practices for scalability and maintainability. Deep knowledge of AI‑specific technologies, including vector databases, embedding models, LLM APIs, and orchestration frameworks.
• Experience establishing organization‑wide best practices for prompt engineering. These practices include systematic testing and version control, comprehensive evaluation frameworks that combine automated metrics with human assessment, model observability including tracking of costs and performance, and performance benchmarking methodologies. The latter enable data‑driven optimization decisions.
• Deep familiarity with responsible AI principles is essential, including fairness, accountability, transparency, and ethics. Understanding of governance considerations for AI systems is also crucial, including model risk management and validation requirements. Practical experience addressing deployment challenges in regulated environments is necessary, including testing, documentation, change management, and ongoing monitoring requirements.

Preferred Qualifications

• Include the technical direction across organizational boundaries. Proven mentoring and coaching abilities develop engineering talent. Strong cross‑functional collaboration skills enable effective partnership with various stakeholders, including product management, data science, design, security, compliance, and business stakeholders.
• Experience in healthcare industries.

Additional Information

This is a Hybrid office position where employees primarily operate from the company office with occasional work from home to support focus work and work/life balance needs. Designated Tech Market locations for this role: New York City; Louisville, KY; Dallas, TX.

Scheduled Weekly Hours: 40

Pay Range: $170,800 - $234,800 per year. This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.

Description Of Benefits

Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole‑person well‑being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave), short‑term and long‑term disability, life insurance and many other opportunities.

About Us

Humana Inc. (NYSE: HUM) is a leading U.S. healthcare company. Through our Humana insurance services and our CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare and Medicaid, families, individuals, military service personnel, and communities at large. Learn more about what we offer at Humana.com and at CenterWell.com.

Equal Opportunity Employer

It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.