Manager - FWA AI Engineer

Job Description Role Purpose: The FWA AI Engineer is responsible for designing, building, testing, and deploying AI solutions that support Fraud, Waste & Abuse (FWA) detection and investigation. The role translates approved business requirements and data science models into scalable production solutions, integrating AI capabilities into operational workflows while ensuring reliability, performance, and business value. Working closely with the FWA Lead, FWA Data Scientist, investigators, and business stakeholders, the role drives the successful implementation and continuous enhancement of FWA AI use cases. Key Accountabilities: 1- FWA Solution Design & Feasibility Assessment; • Assess technical feasibility of approved FWA use cases. • Evaluate data availability, technical requirements, and implementation complexity. • Define solution approaches for AI-driven FWA detection. • Develop technical requirements and implementation plans. • Recommend optimal AI techniques and technologies for delivery. 2- AI Solution Development & Engineering; • Build and optimize AI solutions supporting FWA detection and investigation activities. • Translate models and analytical logic into scalable cloud-native solutions on GCP. • Implement machine learning, anomaly detection, graph analytics, and document intelligence solutions. • Develop APIs, scoring services, and integration components required for operational deployment. • Ensure solutions are scalable, maintainable, and aligned with technical standards. • Collaborate with the FWA Data Scientist to operationalize validated models. 3- Production Deployment & Integration; • Deploy AI solutions into operational environments. • Integrate AI outputs with internal systems and workflows. • Coordinate with Data Engineering and TIDES (IT) teams to enable production deployment. • Support release planning and deployment activities. • Ensure successful transition from development to production. 4- Testing & Quality Assurance; • Conduct technical testing of AI solutions. • Support user acceptance testing with business stakeholders. • Validate output accuracy, reliability, and performance. • Identify and resolve implementation issues. • Ensure solutions meet defined business and technical requirements. 5- Solution Maintenance & Continuous Improvement; • Support ongoing enhancement of deployed FWA AI solutions. • Implement model improvements identified by the Data Science team. • Address technical issues and optimize solution performance. • Incorporate new fraud detection capabilities and business requirements. • Maintain technical documentation and solution knowledge assets. Skills • Python • SQL • Google Cloud Platform (GCP) • Vertex AI • Machine Learning Implementation • AI Solution Development • API & System Integration • NLP & LLM Applications • Document Intelligence & OCR • Software Development Best Practices • Testing & Quality Assurance • Problem Solving • Stakeholder Management • Communication & Presentation Skills • Cross-Functional Collaboration • Experience delivering AI/ML solutions into production environments. • Experience with Google Cloud Platform (GCP) is preferred. • Experience in health insurance, claims, FWA, risk analytics, or financial crime analytics is preferred. • Knowledge of healthcare claims, provider, and pre-authorization processes is advantageous. • Knowledge of SAS is advantageous. Education Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science Show more Show less

Bachelor's Degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science

Role Purpose: Design, build, test, and deploy AI solutions for Fraud, Waste & Abuse (FWA) detection and investigation. Translate business requirements and data science models into production solutions. Work with FWA Lead, FWA Data Scientist, investigators, and stakeholders to implement and enhance FWA AI use cases. Accountabilities include: FWA solution design & feasibility assessment; AI solution development & engineering (cloud-native on GCP, APIs, scoring services); production deployment & integration; testing & quality assurance; solution maintenance & continuous improvement.