AI Automation Engineer

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. 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.