Head of Data & ML Platform & Engineering Site Lead

• We are seeking a strategic and technically savvy leader to head our Data and ML Platformteam
• As the head, you will be responsible for defining and building our ML ExperimentationPlatform from the ground up, while scaling our data and ML infrastructure to support variousroles including Data Platform Engineers, Data Scientists, and Threat Analysts
• Your keyresponsibilities will involve overseeing the design, implementation, and maintenance of scalableML pipelines for data preparation, cataloging, feature engineering, model training, modelserving, and in-field model performance monitoring
• These efforts will directly influence criticalbusiness decisions
• In this role, you'll foster a production-focused culture that effectively bridgesthe gap between model development and operational success
• Furthermore, you'll be at theforefront of spearheading our ongoing Generative AI investments
• The ideal candidate for thisposition will combine strategic vision with hands-on technical expertise in machine learning anddata infrastructure, driving innovation and excellence across our data and ML initiatives
• We are building this team with ownership at Bengaluru, India, this leader will help us boot strapthe entire site, starting with this team

What you'll Do:

Strategic Leadership

• Define the vision, strategy and roadmap for the organization s data and MLplatform to align with critical business goals.
• Help design, build, and facilitate adoption of a modern Data+ML platform
• Stay updated on emerging technologies and trends in data platform, ML Ops andAI/ML

Team Management

• Build a team of Data and ML Platform engineers from a small footprint acrossmultiple geographies
• Foster a culture of innovation and strong customer commitment for both internaland external stakeholders

Platform Development

• Oversee the design and implementation of a platform containing data pipelines,feature stores and model deployment frameworks.
• Develop and enhance ML Ops practices to streamline model lifecycleManagement from development to production.

Data Governance

• Institute best practices for data security, compliance and quality to ensure safeand secure use of AI/ML models.

Stakeholder engagement

• Partner with product, engineering and data science teams to understandrequirements and translate them into platform capabilities.
• Communicate progress and impact to executive leadership and key stakeholders.

Operational Excellence

• Establish SLI/SLO metrics for Observability of the Data and ML Platform alongwith alerting to ensure a high level of reliability and performance.
• Drive continuous improvement through data-driven insights and operationalmetrics.

What you'll Need:

• B.S. in Computer Science, Data Science, Statistics, Applied Mathematics, or a relatedfield and 10+ years related experience; or M.S. with 8+ years of experience; or Ph.D with6+ years of experience.
• 10+ years experience in data engineering, ML platform development, or related fieldswith at least 5 years in a leadership role.
• Familiarity with typical machine learning algorithms from an engineering perspective;familiarity with supervised / unsupervised approaches: how, why and when labeled datais created and used.
• Knowledge of ML Platform tools like Jupyter Notebooks, NVidia Workbench, MLFlow,Ray, Vertex AI, etc
• Experience with modern ML Ops platforms such as MLFLow, Kubeflow or SageMakerpreferred.Experience in data platform product(s) and frameworks like Apache Spark, Flink orcomparable tools in GCP and orchestration technologies (eg Kubernetes, Airflow). Experience with Apache Iceberg is a plus.
• Deep understanding of machine learning workflows, including model training,deployment and monitoring.
• Familiarity with data visualization tools and techniques.
• Experience with boot strapping new teams and growing them to make a large impact.
• Experience operating as a site lead within a company will be a bonus.
• Exceptional interpersonal and communication skills. Work with stakeholders acrossmultiple teams and synthesize their needs into software interfaces and processes.