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About This Role
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Most AI engineering jobs are a thin wrapper around a model API. This role is different.
We build the platform that transforms millions of clinical documents into trusted, actionable data. Our systems use large language models (LLMs) to read medical records, extract structured facts, answer complex questions with citations back to the source document, and route ambiguous cases to human experts for review.
Our users make decisions that impact real healthcare outcomes, so “good enough” is not good enough. Building AI systems that are accurate, reliable, auditable, and scalable is at the core of this role.
As a Senior AI Applied Engineer, you will design, build, deploy, and operate production AI systems used at scale within one of the largest health insurers in the United States. You will own solutions end-to-end, from user experience and APIs to model orchestration, evaluation frameworks, infrastructure, and production operations.Why Join Us
- Build production AI systems where LLMs are in the critical path, not just demos or proofs of concept.
- Work on extraction, retrieval, agentic workflows, and human-review systems that process real healthcare data at scale.
- Own projects end-to-end across frontend, backend, AI orchestration, infrastructure, deployment, and operations.
- Solve challenging problems around accuracy, explainability, traceability, and reliability in regulated environments.
- Ship quickly in a small, high-impact team that embraces AI-assisted development and rigorous quality standards.
- Build systems that continuously improve through expert feedback, evaluations, and human-in-the-loop workflows.
Key Responsibilities
- Design, develop, and deploy full-stack AI-powered applications, including frontend experiences, APIs, backend services, and data pipelines.
- Build and optimize LLM-based workflows for information extraction, classification, retrieval, summarization, and agentic processes.
- Develop robust prompt strategies, structured output frameworks, tool-calling capabilities, and fallback mechanisms to improve reliability.
- Design and implement retrieval-augmented generation (RAG) systems using embeddings, vector databases, and semantic search technologies.
- Build evaluation frameworks, including benchmark datasets, regression testing, quality metrics, and human review workflows.
- Develop human-in-the-loop processes that enable clinicians and domain experts to validate AI outputs and provide feedback for continuous improvement.
- Build and maintain document-processing pipelines, including OCR, document ingestion, metadata extraction, indexing, and search capabilities.
- Optimize AI systems for accuracy, latency, reliability, scalability, and cost efficiency.
- Implement observability, monitoring, logging, alerting, and incident response processes for production systems.
- Deploy and operate services in cloud-native environments using Kubernetes, Docker, CI/CD pipelines, and modern infrastructure tooling.
- Collaborate with product, engineering, clinical, and operational stakeholders to translate complex business requirements into scalable solutions.
- Ensure solutions meet privacy, security, compliance, and auditability requirements within a regulated healthcare environment.
Use your skills to make an impact
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or another quantitative or related technical discipline, or an equivalent combination of education and practical experience.
- 5+ years of experience building and operating production software systems.
- Experience developing backend services, APIs, distributed systems, or data-intensive applications.
- Hands-on experience integrating LLMs into production applications beyond simple chat interfaces, including structured outputs, tool calling, retrieval-augmented generation (RAG), agents, or multi-step workflows.
- Experience with at least one major AI platform or model provider such as OpenAI, Anthropic, Google, Azure AI, or AWS Bedrock.
- Strong programming skills in Python and/or TypeScript/JavaScript.
- Experience debugging, monitoring, and supporting production applications used by real customers.
- Ability to work independently, navigate ambiguity, and take ownership of projects from concept through deployment.
Preferred Qualifications
- Experience building AI systems where LLMs are part of the critical production workflow.
- Experience designing evaluation frameworks, testing strategies, prompt versioning, tracing, and model performance monitoring.
- Experience with agentic architecture, Model Context Protocol (MCP), tool orchestration frameworks, or autonomous workflows.
- Experience with React, Next.js, modern frontend development, and full-stack application architecture.
- Experience deploying and operating workloads in Kubernetes, Docker, and cloud environments.
- Experience with modern CI/CD practices and infrastructure automation.
- Experience working in regulated industries such as healthcare, financial services, or government.
- Familiarity with privacy, compliance, governance, and data protection requirements.
- Experience using AI-assisted development tools to improve engineering productivity and software quality.
- Strong problem-solving, communication, and collaboration skills.
Success in This Role
Successful candidates are energized by ambiguity, motivated by ownership, and excited to solve difficult problems where correctness matters. You enjoy building systems that are accurate, traceable, and trustworthy, and you view model failures as engineering challenges to be solved rather than limitations to be accepted.
This role is best suited for engineers who want to own meaningful problems end-to-end and see their work directly impact on users and business outcomes.
Technology Stack
- TypeScript
- React / Next.js
- Python
- PostgreSQL
- Gemini on Vertex AI
- OCR and Document AI technologies
- Docker
- Kubernetes
- Modern CI/CD platforms and tooling
Additional Information
This role operates in a highly regulated environment. Responsible handling of sensitive and protected data is a fundamental requirement and an essential part of our engineering culture.
Work Style: This position follows a hybrid work schedule, requiring employees to work three days per week in the office and the remaining days remotely. Qualified candidates must currently reside within, or be willing to relocate to, a commutable distance from one of the talent markets.
#enterpriseAI
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.
$141,100 - $194,000 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 $141K-$194K 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($167K) sits 21% below the category median. Disclosed range: $141K to $194K.
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
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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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