AI LEAD

Responsibilities

Strategic Leadership o Define and own the enterprise data science vision, roadmap, and operating model aligned with company strategy.

Translate business priorities into high-impact analytics, AI, and machine learning initiatives. Ability to implement guardrails, content safety, and AI governance policies. - Experience defining quality metrics and evaluating AI performance. - Knowledge of Azure DevOps, Github repository - Able to map the Model requirement based on use case Working experience Generative AI, RAG design pattern, Semantic Search and Azure AI ecosystem.

o Serve as an executive advisor on data-driven decision-making, AI adoption, and emerging technologies.

o Establish success metrics and ROI measurement for data science initiatives. Organization Leadership o Build, mentor, and scale a diverse, high-performing organization of data scientists and ML engineers. o Set clear expectations, career paths, and performance standards. o Foster a culture of curiosity, rigor, and collaboration. o Create an environment where teams are empowered, accountable, and closely connected to the business. Delivery AI and Machine Learning o Oversee the design, development, deployment, and lifecycle management of predictive and prescriptive models. o Ensure solutions are production-grade, integrated into applications and workflows, and supported by strong MLOps practices. o Partner with engineering teams to embed models into clinical and operational systems. o Establish governance, validation, and monitoring processes consistent with HIPAA and HITRUST requirements. Hands-On Technical Leadership o Maintain a hands-on approach to data science and AI, including the ability to write, review, and guide code in modern data science and machine learning stacks. o Stay close to the work by participating in model design, experimentation, and technical decision-makingespecially for high-impact or clinically sensitive use cases. o Provide technical leadership and mentorship by reviewing approaches, validating assumptions, and ensuring analytical rigor and model quality. o Partner with data scientists and ML practitioners to unblock complex prob