Senior AI Automation Engineer
About The Position
Alignment Health is seeking a senior individual contributor and technical leader for its Data & Technology Solutions team. The role architects, builds, and scales intelligent AI systems and enterprise automation solutions to improve care quality and operational performance across the company’s Medicare Advantage business. The engineer partners with AI Scientists, Data Engineers, Product Managers, Clinical Operations, and Application Engineering teams to deliver production AI systems for areas including claims processing, risk adjustment, prior authorization, revenue integrity, and member engagement. The role has substantial independent ownership and technical authority, including leading complex initiatives from concept through production and elevating engineering practices across the team. The role leads development of distributed, cloud-native systems and services, APIs, and microservices; establishes practices for reliability and scaling; and builds integrations between cloud and non-cloud systems, including legacy data sources and multimodal data. It owns CI/CD, containerization, deployment and infrastructure-as-code standards, as well as data and automation pipeline reliability and RPA strategy. The engineer applies AI/ML to production systems rather than conducting model research or training. Responsibilities include building and tuning inference pipelines, integrating LLMs into workflows, and productionizing Databricks-based models and pipelines. The role also defines monitoring, alerting, and testing frameworks, leads post-incident reviews, and provides technical mentorship and leadership without formal direct reports. The position requires a bachelor's degree in a quantitative or related field, or equivalent progressive senior-level experience. A master's degree and relevant cloud or RPA certifications are preferred. No license is required at this time. The pay range is $172,364.00–$258,547.00, with compensation potentially varying based on factors such as market location, education, responsibilities, and experience.
Requirements
- 5–8 years of professional software engineering experience, with a demonstrated focus on distributed systems, cloud infrastructure, or data platforms.
- Proven ability to independently own and deliver complex, production-grade distributed systems from concept through deployment and operations.
- Experience building and scaling hybrid infrastructure that connects cloud-native and non-API or legacy systems.
- Demonstrated experience serving and integrating AI/ML models or LLMs in production, including performance tuning; model training and research are not the focus.
- Experience in a regulated industry such as healthcare, insurance, or financial services, with working knowledge of compliance and security requirements.
- Experience building and scaling automation solutions using RPA platforms and workflow orchestration tools, including ROI governance.
- Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related quantitative field, or equivalent progressive senior-level experience.
- No license is required at this time.
Nice To Haves
- Experience with Databricks, including Delta Lake, MLflow, and Unity Catalog, at a senior or platform-owner level.
- Experience applying AI/ML to complex healthcare data such as claims, revenue cycle, prior authorization, medical coding, or clinical text.
- Familiarity with HL7 FHIR, ICD-10/CPT, 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.
- Master's degree in Computer Science or a related quantitative discipline.
- Advanced or specialty cloud certification (AWS, Azure, or GCP), or RPA platform certification.
Responsibilities
- Architect, build, and scale intelligent AI systems and enterprise automation solutions for the Medicare Advantage business.
- Lead development of distributed, cloud-native systems, including services, APIs, and microservices, and set team practices for fault tolerance, retries, idempotency, queuing, eventing, caching, and horizontal scaling.
- Build integrations between cloud and non-cloud systems, including flat files, legacy databases, EDI feeds, mainframes, streaming data, documents, images, and audio; establish integration patterns for the team.
- Own CI/CD, containerization, deployment, and infrastructure-as-code standards, and drive adoption of Docker, Kubernetes, and CI/CD best practices.
- Build and govern scalable ETL and orchestration pipelines and own automation strategy using RPA platforms and orchestration tools, including ROI governance and fault-tolerant workflow standards.
- Build production inference pipelines for batch, real-time, and streaming workloads; partner with AI Scientists to bring models into low-latency, high-throughput production systems and own benchmarking and performance tuning.
- Define the technical approach for integrating LLMs into workflows, including prompt engineering, RAG, and multi-agent orchestration frameworks.
- Productionize models and pipelines built on Databricks, including Delta Lake, MLflow, and Unity Catalog; own CI/CD, orchestration, access controls, cost tuning, and platform usage standards.
- Define alerting, testing, and monitoring frameworks to detect degradation before it affects member outcomes, and lead post-incident reviews.
- Mentor engineers through code reviews, design critiques, and paired problem-solving; contribute to hiring, technical interviews, engineering standards, and responsible AI practices, including bias detection, explainability, and governance.
- Guide the technical direction of junior and mid-level engineers on project teams as an informal technical lead.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
