• Lead the end-to-end delivery of AI MVPs, from use case definition through demonstration and iteration, including engaging directly with clients. • Design solution architectures and technical roadmaps that integrate AI components within client cloud environments. • Oversee model development, validation, and user interface integration to create functional prototypes. • Deliver MVP demonstrations, gather stakeholder feedback, and refine solutions through post-deployment feedback loops. • Construct and maintain scalable data pipelines for processing, transforming, and feeding data into AI models and applications. • Lead agile development sprints and facilitate SCRUM ceremonies to ensure quality, velocity, and stakeholder alignment. • Mentor developers and data scientists, fostering collaboration and excellence in applied AI engineering. • Support backlog management, sprint prioritization, and delivery tracking across multidisciplinary teams. • Apply Generative AI and Large Language Model (LLM) techniques to automate, analyze, and enhance compliance and operational workflows. • Evaluate and refine prompt engineering and context enrichment strategies for AI-based applications. • Assess and optimize existing AI toolchains and workflows to improve scalability and delivery efficiency. • Advise on production deployment strategies, including security, performance, and data integration considerations. • Conduct operational readiness assessments to evaluate solution performance, resilience, and maintainability. • Design lightweight model monitoring and retraining pipelines to maintain relevance and accuracy. • Support AI governance alignment, ensuring all solutions adhere to organizational standards and risk management practices. • Serve as the primary client interface for technical leadership, solution assurance, and delivery success. • Collaborate with cross-functional teams to rapidly prototype and iterate on solutions. • Deploy and manage applications and models in cloud environments leveraging infrastructure-as-code and DevOps best practices.