Senior AI Automation Engineer

🕒 June 18

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Alignment Health

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2013

⚕️ Healthcare Insurance

💰 $135M Series C on 2020-03

Healthcare Insurance • Insurance • Healthcare

Alignment Health is dedicated to providing comprehensive care for Medicare members, emphasizing the needs of seniors, the chronically ill, and those who are frail. With a mission to transform senior healthcare, Alignment Health leverages a tailored care model and advanced technology to deliver high-quality, low-cost healthcare services. Their 24/7 concierge care team collaborates with trusted local providers to ensure that every member receives personalized care, reflecting the company's commitment to treating all members as valued family members.

📋 Description

• 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.

🎯 Requirements

• 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.

🏖️ Benefits

• Competitive salary • Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development opportunities