• Technical Leadership: Define architecture standards for Generative AI solutions, driving design decisions that impact multiple squads and products. • Conversational Solutions Development: Design, develop, and integrate end-to-end AI solutions with a focus on scalability and maintainability. • AI Agents & Automation: Build and evolve AI agents and intelligent automations, including multi-agent workflows where applicable, to solve real business and engineering problems at scale. • AI-Assisted Engineering: Explore and implement AI-assisted development practices (code generation, refactoring, testing assistance) to improve our software development lifecycle. • AI Security & Governance: Define and implement robust security strategies for language models, with a special focus on mitigating risks such as prompt injection and sensitive data leakage (ensuring compliance with healthcare/HIPAA regulations). • System Integration: Connect Generative AI models with internal APIs, establishing reusable integration patterns for the team. • POC Lifecycle Management: Lead POCs from technical ideation through validation, QA, and production transition, including feasibility assessment and architecture trade-off decisions. • Quality & Testing: Define and evolve testing pipelines for non-deterministic flows (Generative AI), ensuring accuracy in benefits and eligibility responses. • Technical Mentorship: Support the growth of mid-level and junior engineers through code reviews, pair programming, and knowledge sharing. • Cross-functional Collaboration: Act as the technical reference in discussions with Product Managers, technical leadership, and QA teams, translating business goals into architectural decisions.

• Develop, implement, and maintain APIs and backend services using the .NET platform (Core/.NET). • Collaborate on the architecture of new features and on the continuous improvement of existing systems. • Ensure code quality through unit and integration tests and development best practices (SOLID, Clean Architecture). • Implement and manage observability solutions (metrics, logs, traces) using tools such as Dynatrace to monitor application health and performance in production. • Work with cloud infrastructure, deploying and managing services on Google Cloud Platform (GCP), with a focus on Google Kubernetes Engine (GKE). • Actively participate in agile ceremonies (Scrum/Kanban) and code reviews. • Identify performance bottlenecks and propose optimization solutions.

• Perform back-end development, fulfilling requirements according to client solutions, standards, and best practices.

• Development of data processing algorithms for remote identification/authentication. • Improvement and development of internal tools for compliance. • Test-driven development and A/B testing. • Optimization and automation of CI/CD pipelines. • Monitoring and performance tuning of cloud infrastructure.