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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 Artificial Intelligence \& Automation Engineer is a hands\-on technical contributor on the Data \& Technology Solutions team, responsible for designing, building, and deploying intelligent AI systems and automated workflows that drive operational efficiency and elevate the quality of care for our Medicare Advantage members. You will partner closely with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams to translate complex business problems — from claims processing and prior authorization to member communication and revenue integrity — into scalable, production\-grade solutions. This role directly influences our ability to reduce administrative burden, accelerate payment accuracy, and create smarter, faster member experiences.Job Duties / Responsibilities
- Design and deploy production\-grade AI and machine learning models. Build, test, and deploy predictive and generative AI models — including NLP, classification, regression, and computer vision — into cloud\-based environments optimized for Medicare Advantage workloads such as claims processing, risk adjustment, and member communication. Maintain rigorous monitoring and drift detection to preserve model integrity over time.
- Build andoperateintelligent process automation solutions. Identify and automate high\-volume, repetitive business processes using RPA platforms (e.g., UiPath, Power Automate) and orchestration tools (e.g., Airflow, Prefect). Evaluate automation ROI, build fault\-tolerant workflows with proper error handling and alerting, and maintain automation bots and pipelines as business needs evolve.
- Develop andmaintainAI/ML data infrastructure and pipelines. Build robust ETL and feature engineering pipelines that support model training, validation, and real\-time inference. Manage AI/ML infrastructure to enable efficient experimentation, retraining, and monitoring so that models perform accurately and equitably in our regulated production environment.
- Integrate AI models and automation services into enterprise products. Connect deployed models and automation services into existing systems via REST APIs and microservices, ensuring full adherence to HIPAA security and privacy standards. Containerize and deploy using Docker and Kubernetes with CI/CD pipelines that ensure reproducible, reliable releases.
- Implement LLM\-powered applications and generative AI capabilities. Integrate large language models into clinical and operational workflows using prompt engineering, fine\-tuning, and retrieval\-augmented generation (RAG). Leverage agentic AI frameworks to build multi\-step automation solutions that deliver end\-to\-end workflow transformation for high\-impact processes such as clinical document intelligence and member engagement.
- Monitor,optimize, and ensure system reliability. Continuously test and tune models, pipelines, and automation bots to improve accuracy, latency, and scalability. Implement monitoring and alerting frameworks to proactively identify degradation or data quality issues before they affect member outcomes or operational throughput.
- Collaborate cross\-functionally and advance a culture of AI innovation. Partner with AI Scientists, Product Managers, Clinical stakeholders, and business analysts to translate requirements into technical specifications. Contribute to knowledge sharing through documentation and code reviews, and evaluate emerging AI technologies — including responsible AI practices such as bias detection and model explainability.
Supervisory Responsibilities
This is an individual contributor role with no direct supervisory responsibility. The engineer is expected to provide informal technical mentorship to peers and junior team members through code reviews, documentation, and collaborative problem\-solving.
Job Requirements
Experience
*Required:*
- 3–5 years of professional experience in software engineering with a demonstrated focus on AI/ML, data science, or intelligent process automation
- Hands\-on experience building and deploying machine learning models in a production cloud environment (AWS, Azure, or GCP)
- Demonstrated experience with the full AI/ML model lifecycle: data preparation, training, validation, deployment, and monitoring
- Experience in a regulated industry (healthcare, insurance, or financial services) with working knowledge of compliance requirements in production AI systems
- Experience developing and deploying automation solutions using RPA platforms or workflow orchestration tools
*Preferred:*
- Experience applying AI, NLP, or ML to healthcare data including claims processing, revenue cycle management, prior authorization, medical coding, or clinical text understanding
- Familiarity with healthcare data standards including HL7 FHIR, ICD\-10/CPT codes, or DICOM
- Experience in a Medicare Advantage, managed care, or payer environment
Education
*Required:*
- Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field
- Equivalent combination of education and demonstrated, progressive hands\-on experience will be considered
*Preferred:*
- Master's degree in Computer Science, AI/ML, or a related quantitative discipline
Training
*Required:*
- Demonstrated proficiency with Python and ML frameworks through professional work experience; formal training or self\-directed equivalent accepted
- Working knowledge of cloud AI/ML services (AWS SageMaker, Azure AI, or Google Vertex AI); training or certification equivalent accepted
*Preferred:*
- Completion of formal certification or coursework in MLOps, cloud AI platforms, responsible AI, or agentic AI development
- Certification in RPA platforms (UiPath, Automation Anywhere, or Microsoft Power Automate)
Skills \& Competencies
Technical / Role\-Specific Skills
- AI/ML Engineering: Proficiency in Python (primary), with strong command of ML frameworks including PyTorch, TensorFlow, scikit\-learn, and Hugging Face Transformers; experience with LLM\-based frameworks including RAG pipelines, vector databases, and agentic frameworks such as LangChain or LangGraph
- Intelligent Process Automation: Hands\-on experience with at least one RPA platform (UiPath, Power Automate, Automation Anywhere) and at least one orchestration tool (Apache Airflow, Prefect, or n8n); ability to analyze processes, identify automation opportunities, and estimate ROI
- Cloud AI Platforms: Hands\-on experience with AWS SageMaker, Azure AI / Document Intelligence, Google Cloud Vertex AI, or Databricks; familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines for ML workflows
- Data Engineering \&MLOps: Ability to build and maintain ETL and feature pipelines; proficiency with GitHub and model versioning tools (MLflow or equivalent); working knowledge of SQL for data transformation and workflow support
- Healthcare \& Regulatory Literacy: Working knowledge of HIPAA compliance and data security requirements in regulated healthcare environments; familiarity with healthcare data standards including HL7 FHIR and ICD\-10/CPT as applied to AI/ML systems
- API Design \& System Integration: Experience integrating AI models and automation services into enterprise applications via REST APIs and microservices; ability to collaborate with software engineering teams to ensure production\-grade reliability
- Responsible AI Principles: Awareness of and commitment to ethical AI practices including bias detect
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: $130,332\.00 \- $195,498\.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 $130K-$195K range is below 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($162K) sits 12% below the category median. Disclosed range: $130K to $195K.
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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