Data and AI Associate Director

At Zimmer Biomet, we believe in pushing the boundaries of innovation and driving our mission forward. As a global medical technology leader for nearly 100 years, a patient’s mobility is enhanced by a Zimmer Biomet product or technology every 8 seconds. As a Zimmer Biomet team member, you will share in our commitment to providing mobility and renewed life to people around the world. To support our talent team, we focus on development opportunities, robust employee resource groups (ERGs), a flexible working environment, location specific competitive total rewards, wellness incentives and a culture of recognition and performance awards. We are committed to creating an environment where every team member feels included, respected, empowered and recognised.

What You Can Expect

The Associate Director of Artificial Intelligence provides strategic, technical, and people leadership for AI initiatives across the organization. This role leads teams of AI Researchers, Applied Scientists, MLOps Engineers, and Data Scientists to design, build, and operationalize advanced AI solutions that deliver measurable business impact. The role balances research innovation, applied delivery, and platform maturity, ensuring AI capabilities are scalable, governed, and aligned with enterprise strategy.

Work Location: Bangalore Work Mode: Hybrid (3 Days in office) Shift Timing - 8:00 AM - 5:00 PM

How You'll Create Impact AI Strategy & Technical Leadership • Define and execute AI strategy aligned with business priorities and long-term technology roadmaps • Lead development of advanced AI capabilities including machine learning, generative AI, and advanced analytics • Translate emerging research into practical, scalable AI solutions • Partner with Architecture and Platform teams to evolve AI infrastructure and tooling People Leadership & Talent Development • Lead, coach, and develop high-performing teams of AI professionals • Set clear goals, performance expectations, and career development plans • Drive hiring, workforce planning, and capability growth across AI disciplines • Foster a culture of innovation, rigor, and responsible AI Delivery & Execution Oversight • Oversee delivery of AI initiatives from experimentation through production • Ensure quality, reliability, and scalability of AI solutions • Manage dependencies across Data Engineering, Cloud, Security, and Product teams • Monitor outcomes, KPIs, and value realization MLOps, Platform & Governance • Ensure strong MLOps practices including model lifecycle management, monitoring, and retraining • Establish AI governance, risk management, and responsible AI practices • Partner with Security, GRC, and Legal to ensure compliance and ethical AI usage Stakeholder & Executive Engagement • Act as a trusted advisor to senior business and technology leaders • Communicate AI strategy, progress, and risks to executive audiences • Represent AI capabilities in enterprise planning and prioritization forums

What Makes You Stand Out Core Competencies • Strategic thinking and technical depth • Strong people leadership and talent development • Ability to translate research into business outcomes • Executive-level communication and influence • Ownership mindset for scalable and responsible AI

Your Background Education • Bachelor’s degree in Computer Science, Engineering, Data Science, or related field required • Master’s degree or Ph.D. in AI, Machine Learning, or related field preferred Years of Experience • 10–14 years of progressive experience in AI, machine learning, or advanced analytics • 5+ years in people management or technical leadership roles • Experience leading production AI systems in enterprise environments • Demonstrated experience scaling AI teams and platforms

Technologies & Tools Machine Learning & AI • Machine learning frameworks: PyTorch, TensorFlow, JAX • Classical ML and deep learning techniques • Generative AI and foundation model concepts • Model evaluation, explainability, and bias mitigation techniques MLOps & AI Platforms • MLflow, Kubeflow, or equivalent ML lifecycle tools • Feature stores and model registries • Model monitoring and drift detection solutions Data & Engineering • Data processing frameworks (e.g., Spark) • SQL and data warehousing platforms • Data pipelines and orchestration tools (Airflow, Dagster, Prefect) Cloud & Infrastructure • Cloud platforms: AWS, Azure, or GCP • Containerization: Docker • Orchestration: Kubernetes • Infrastructure as Code (Terraform, ARM/Bicep, CloudFormation) DevOps, Security & Governance • CI/CD pipelines for ML and data workloads • Identity, access control, and secrets management • Responsible AI, model governance, and auditability frameworks

EOE/M/F/Vet/Disability