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About This Role
Become a part of our caring community
At Humana, we are on a mission to provide an exceptional experience for our community of developers, testers, product owners, and business partners that deliver software products for our members.
We believe that happy and productive delivery teams create essential features that enhance our members' well-being and overall experience with our Humana products.
As the Associate Director of AI Enablement in the Enterprise Engineering Enablement organization, you will play a crucial role in building Humana's Agentic Development Lifecycle (ADLC) — an AI-native software delivery platform program with four dedicated tracks, a published governance framework, and hard targets for reuse, uptime, cost, and regulatory defensibility in a HIPAA-regulated environment.
Your work will be transformational for our company as we seek to enable enhanced productivity across a wide range of IT disciplines. The ideal candidate for this role will need to have a unique mix of leadership experience, proficiency with applying modern Agile, Engineering, and DevOps practices to enable software delivery, strong public speaking skills, and be exceptional at leading people through change. You will need to have the skillset to apply your software delivery expertise to an entire IT organization, be the catalyst for an enterprise-wide organizational change effort, and simultaneously ensure smooth product and platform engineering delivery for your own teams responsibilities. You will need to be comfortable working within and leading a matrixed team where influence is more important than official reporting structure. Finally, you will need to have a passion for enablement, coaching, and teaching in order to assist agile and operations teams with adopting new technologies and new ways of working using GAI.
Use your skills to make an impact
Required:
- 15+ years in software engineering, with 5+ years leading engineering managers, leads, or multiple squads
- Demonstrated ownership of a multi-track or platform-scale program (3+ parallel workstreams, 25+ engineers)
- Hands-on depth in at least two of: distributed systems, platform/SRE engineering, ML/AI engineering, cloud infrastructure
- Production experience with Kubernetes and at least one major cloud (GCP, Azure, or AWS)
- Working fluency with LLM application development — agent frameworks, prompt engineering, RAG, tool/function calling, evaluation methodology
- Track record of shipping in a regulated environment with real security and compliance constraints
- Proven ability to drive alignment across peer organizations without direct authority
- You have experience with enterprise-grade engineering platforms similar to Azure DevOps Services, GitHub, SonarQube, Artifactory, TFE or their industry standard equivalents.
- You have experience with cloud technologies, Infrastructure as Code (IaC), Kubernetes, and emerging DevOps trends like 'GitOps' combined with 'pull' based deployment technologies like Argo and Terraform Enterprise (TFE).
- You have a deep understanding of large language models, including how they are trained, and are knowledgeable about trends in this quickly changing technology landscape
- Excellent written communication — you can produce an architecture doc and a VP-ready one-pager with equal comfort
- You are a caring and compassionate leader focused on enabling team members to grow and achieve their best effort in their role on the team.
- You enable a culture of collaboration, empowerment, and team cohesiveness where team members are excited about their work and understand the value that they are bringing to the enterprise.
- You are able to give meaningful and direct feedback to your direct reports in order to promote team member growth and maturity.
- You value work/life balance and enable a positive team culture that people want to be a part of.
- You are a strong communicator, a connector, and enjoy a highly agile work environment. Being the leader of AI Enablement @ for Humana IT will put you right in the middle of some amazing transformational work.
- You enjoy creating presentations, bringing awareness to newly delivered features, and communicating progress to senior leaders.
The 30% Hands-On
This role is explicitly designed so that technical credibility is not optional. You will personally:
- Review architecture decisions, ADRs, and agent design specs — and write them when the situation calls for it
- Prototype in the agent harness: sub-agent definitions, tool/MCP integrations, prompt and character specifications
- Read traces, evaluate eval-harness output, and debug agent behavior alongside your engineers
- Contribute to reference implementations that the Factory PODs will pattern from
- Sit in code and design reviews as a participant, not an observer
If you have not shipped production code or architecture in the last 24 months, this is likely not the right role for you.
Strongly Preferred
- You hold a Master's degree.
- Experience with agentic AI in production — multi-agent orchestration, supervisor/sub-agent patterns, agent observability, drift detection
- Familiarity with Claude Code, the Claude Agent SDK, MCP (Model Context Protocol), or comparable agent tooling
- Background in healthcare, payer, or life sciences with direct HIPAA/PHI handling experience
- Exposure to AI governance frameworks — NIST AI RMF, ISO 42001, EU AI Act, or internal model risk management
- FinOps experience — cost telemetry, chargeback, and optimization for cloud or AI workloads
- Experience modernizing legacy estates or driving developer-productivity programs at enterprise scale
Work Location:
- Primary Location: Louisville, KY
- Additional Locations: + Atlanta, GA + Boston, MA + Charlotte, NC + Arlington, VA + Fort Lauderdale, FL + Louisville, KY + New York, NY + Tampa, FL
Reporting Information:
- You'll report to the Director of AI Enablement.
- You'll be part of Enterprise Engineering Services organization.
Scheduled Weekly Hours
40Pay Range
The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.
$142,300 - $195,700 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, paid parental and caregiver leave), short-term and long-term disability, life insurance and many other opportunities.About Us
About Humana: 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.
Salary Context
This $142K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Humana, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($169K) sits 20% below the category median. Disclosed range: $142K to $195K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Humana AI Hiring
Humana has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Louisville, KY, US, New York, NY, US, Remote, US. Compensation range: $177K - $284K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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