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About This Role
Alignment Health is breaking the mold in conventional health care, committed to serving seniors and those who need it most: the chronically ill and frail. It takes an entire team of passionate and caring people, united in our mission to put the senior first. We have built a team of talented and experienced people who are passionate about transforming the lives of the seniors we serve. In this fast\-growing company, you will find ample room for growth and innovation alongside the Alignment Health community. Working at Alignment Health provides an opportunity to do work that really matters, not only changing lives but saving them. Together.
The Senior AI \& Automation Engineer is a senior individual contributor and technical leader on the Data \& Technology Solutions team, responsible for architecting, building, and scaling intelligent AI systems and enterprise automation solutions that directly improve care quality and operational performance across our Medicare Advantage business. You will serve as a go\-to technical expert for AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams — driving end\-to\-end delivery of production AI systems that span claims processing, risk adjustment, prior authorization, revenue integrity, and member engagement. This role carries a higher degree of independent ownership and technical authority than the mid\-level equivalent, with expectations to lead complex, ambiguous initiatives from concept through production and to actively elevate the engineering practices of the broader team.Job Duties / Responsibilities
- Architect and deliver production\-grade AI and machine learning systems. Lead the end\-to\-end design and deployment of predictive and generative AI models — including NLP, classification, regression, and computer vision — for high\-stakes Medicare Advantage workloads. Own architectural decisions related to model selection, scalability, and production readiness, and establish monitoring and drift detection standards adopted across the team.
- Lead the design and scaling of intelligent process automation. Evaluate and architect enterprise\-wide automation strategies using RPA platforms (e.g., UiPath, Power Automate) and orchestration tools (e.g., Airflow, Prefect). Drive automation ROI analysis, establish engineering standards for fault\-tolerant workflow design, and serve as the senior technical owner for the organization's most complex automation pipelines.
- Own AI/ML data infrastructure strategy and pipeline reliability. Design and govern robust ETL and feature engineering pipelines that support model training, validation, and real\-time inference at scale. Define infrastructure standards for experimentation, retraining, and monitoring that ensure consistent model performance across a regulated, high\-availability production environment.
- Integrate AI and automation systems into enterprise architecture. Lead the integration of deployed models and automation services into enterprise products via REST APIs and microservices, setting the engineering bar for security, HIPAA compliance, and maintainability. Drive adoption of containerization (Docker, Kubernetes) and CI/CD best practices across the AI engineering team.
- Architect and implement LLM\-powered and agentic AI applications. Define the technical approach for integrating large language models into clinical and operational workflows — including prompt engineering, fine\-tuning, RAG pipelines, and multi\-agent orchestration frameworks (LangChain, LangGraph, AutoGen). Own delivery of agentic AI solutions that transform end\-to\-end workflows in areas such as clinical document intelligence, intelligent prior authorization, and member communication.
- Establish reliability standards and drive continuous system improvement. Own the performance, reliability, and scalability posture of AI and automation systems across the team. Define alerting, testing, and tuning frameworks that proactively surface degradation before it affects member outcomes, and lead post\-incident reviews to build organizational resilience.
- Mentor engineers and shape AI engineering culture. Provide senior technical mentorship to mid\-level and junior engineers through hands\-on code reviews, design critiques, and paired problem\-solving. Contribute to hiring, technical interviews, and the establishment of team\-wide engineering standards — including responsible AI practices such as bias detection, model explainability, and governance in high\-stakes healthcare contexts.
Supervisory Responsibilities
This role is structured as a senior individual contributor with no formal direct reports. The Senior AI \& Automation Engineer is expected to function as an informal technical lead — providing mentorship, conducting code reviews, participating in hiring panels, and guiding the technical direction of junior and mid\-level engineers on project teams.
Job Requirements
Experience
*Required:*
- 5–8 years of professional experience in software engineering with a demonstrated, progressive focus on AI/ML, data science, or intelligent process automation
- Proven track record of independently owning and delivering complex, production\-grade AI/ML systems from design through deployment and ongoing operations
- Demonstrated experience with the full AI/ML model lifecycle at scale: data architecture, model design, training, validation, deployment, monitoring, and retraining
- Experience in a regulated industry (healthcare, insurance, or financial services) with deep working knowledge of compliance and security requirements in production AI environments
- Experience architecting and scaling automation solutions using RPA platforms and workflow orchestration tools, including cross\-functional stakeholder engagement and ROI governance
*Preferred:*
- Experience applying AI, NLP, or ML to complex healthcare data including claims processing, revenue cycle management, prior authorization, medical coding, or clinical text understanding
- Demonstrated knowledge of healthcare data standards including HL7 FHIR, ICD\-10/CPT codes, or DICOM
- Experience in a Medicare Advantage, managed care, or payer environment at a senior engineering level
- Prior experience as an informal technical lead, principal engineer, or engineering lead on an AI/ML team
Education
*Required:*
- Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
- Equivalent combination of education and demonstrated, progressive senior\-level hands\-on experience will be considered
*Preferred:*
- Master's degree in Computer Science, AI/ML, or a related quantitative discipline
Training
*Required:*
- Advanced, demonstrated proficiency with Python and ML frameworks through professional work experience at a senior level; formal training or equivalent self\-directed mastery accepted
- Hands\-on experience with cloud AI/ML platforms (AWS SageMaker, Azure AI, or Google Vertex AI) at an architecture or senior engineering level; certification or equivalent experience accepted
*Preferred:*
- Formal certification or coursework in MLOps, cloud AI platforms (AWS, Azure, or GCP), responsible AI, or agentic AI development
- Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)
- Participation in advanced AI/ML communities, conferences, or open\-source contributions demonstrating technical depth
Skills \& Competencies
Technical / Role\-Specific Skills
- AI/ML Engineering: Expert\-level Python proficiency with deep command of ML frameworks including PyTorch, TensorFlow, scikit\-learn, and Hugging Face Transformers; advanced experience designing LLM\-based systems including RAG pipelines, vector databases, and multi\-agent orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI)
- Intelligent Process Automation — Architecture \& Governance : Proven ability to architect enterprise\-scale automation strategies using RPA platforms (UiPath, Power Automate, Automation Anywhere) and orchestration tools (Apache Airflow, Prefect, or n8n); experience setting engineering standards, conducting automation ROI governance, and leading complex, cross\-functional automation programs
- Cloud AI Platforms \&MLOps: Expert\-level experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; mastery of containerization (Docker, Kubernetes), CI/CD pipelines, and MLOps tooling (MLflow, Kubeflow, or equivalent) at an architecture and governance level
- Data Engineering \& AI Infrastructure: Ability to design, govern, and scale ETL and feature pipelines across complex, high\-availability healthcare environments; deep proficiency in SQL, data architecture patterns, and real\-time inference infrastructure
- Healthcare \& Regulatory Expertise: Deep working knowledge of HIPAA compliance, data security requirements, and data governance in regulated healthcare environments; advanced familiarity with healthcare data standards including HL7 FHIR and ICD\-10/CPT as applied to production AI and automation systems
- Technical Leadership \& AI Innovation: Demonstrated ability to drive architectural decisions, mentor engineers, lead design reviews, and shape engineering standards across a team; track record of identifying and delivering high\-ROI AI and automation innovations in an enterprise healthcare context
- Responsible AI \&Governance : Applied experience with ethical AI frameworks including bias detection, model explainability (SHAP, LIME, or equivalent), and AI governance practices; ability to embed responsible AI standards into team workflows where AI outputs directly influence member care and financial decisions
License
*Required:* None at this time.
*Preferred:*
- Cloud certification from AWS, Microsoft Azure, or Google Cloud at an advanced or specialty level (AI/ML or data engineering track)
- RPA platform certification (UiPath, Automation Anywhere, or Microsoft Power Automate)
Essential Physical Functions:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
1\. While performing the duties of this job, the employee is regularly required to talk or hear. The employee regularly is required to stand, walk, sit, use hand to finger, handle or feel objects, tools, or controls; and reach with hands and arms.
2\. The employee frequently lifts and/or moves up to 10 pounds. Specific vision abilities required by this job include close vision and the ability to adjust focus.
Pay Range: $172,364\.00 \- $258,547\.00
Pay range may be based on a number of factors including market location, education, responsibilities, experience, etc.
Alignment Health is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, gender identity, or sexual orientation.
- *DISCLAIMER: Please* *beware of recruitment phishing scams affecting Alignment Health and other employers where individuals receive fraudulent employment\-related offers in exchange for money or other sensitive personal* *information. Please* *be advised that Alignment Health and its subsidiaries will never ask you for a credit card, send you a check, or ask you for any type of payment as part of consideration for employment with our company. If you feel that you have been the victim of a scam such as this, please report the incident to the Federal Trade Commission at* *https://reportfraud.ftc.gov/\#/. If you would like to verify the legitimacy of an email sent by or on behalf of Alignment Health’s talent acquisition team, please email* *[email protected].*
Salary Context
This $172K-$258K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Alignment Healthcare, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($215K) sits 16% above the category median. Disclosed range: $172K to $258K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Alignment Healthcare AI Hiring
Alignment Healthcare has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Orange, CA, US. Compensation range: $195K - $297K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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