AI Automation Engineer

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. 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 and operate intelligent 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 and maintain AI/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 by connecting 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 large‑language‑model‑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. Experience and Qualifications Required Experience: 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 and Certifications Required Training: 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 AI/ML Engineering: Proficiency in Python; 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 and at least one orchestration tool; 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 detection. Pay Range $130,332.00 - $195,498.00 (pay range may be based on location, education, responsibilities, experience, etc.) Equal Opportunity Statement 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. #J-18808-Ljbffr E2E Alignment Healthcare USA, LLC