Head of Data ML Platform Engineering Site Lead Hybrid

The charter of the Data ML Platform team is to harness all the data that is ingested and cataloged within the Data LakeHouse for exploration insights model development ML model development lifecycle ML engineering and Insights Activation. This team is situated within the larger Data Platform group which serves as one of the core pillars of our company. We processdata at a truly immense scale. The data sets we process are composed of various facets
including telemetry data associated metadata IT asset information contextual formation about threat exposure and many more. These facets comprise the overall data platform which is currently over 200 PB and maintained in a hyper scale Data Lakehouse.

We are seeking a strategic and technically savvy leader to head our Data and ML Platform team. As the head you will be responsible for defining and building our ML Experimentation Platform from the ground up while scaling our data and ML infrastructure to support various roles including Data Platform Engineers Data Scientists and Threat Analysts. Your key responsibilities will involve overseeing the design implementation and maintenance of scalable ML pipelines for data preparation cataloging feature engineering model training model serving and infield model performance monitoring. These efforts will directly influence critical

business decisions. In this role youll foster a productionfocused culture that effectively bridgesthe gap between model development and operational success. Furthermore youll be at the forefront of spearheading our ongoing Generative AI investments. The ideal candidate for this position will combine strategic vision with handson technical expertise in machine learning and data 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 strap the entire site starting with this team.

What Youll Do:
Strategic Leadership

• Define the vision strategy and roadmap for the organizations data and ML platform to align with critical business goals.

• Help design build and facilitate adoption of a modern DataML platform

• Stay updated on emerging technologies and trends in data platform ML Ops and AI/ML

Team Management

• Build a team of Data and ML Platform engineers from a small footprint across multiple geographies

• Foster a culture of innovation and strong customer commitment for both internal and 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 lifecycle Management from development to production.

Data Governance

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

Stakeholder engagement

• Partner with product engineering and data science teams to understand requirements 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 along with alerting to ensure a high level of reliability and performance.

• Drive continuous improvement through datadriven insights and operational metrics.

What Youll Need:

• B.S. in Computer Science Data Science Statistics Applied Mathematics or a related field and 10 years related experience; or M.S. with 8 years of experience; or Ph.D with 6 years of experience.

• 10 years experience in data engineering ML platform development or related fields with 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 data is 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 SageMaker preferred.Experience in data platform product(s) and frameworks like Apache Spark Flink or comparable tools in GCP and orchestration technologies (e.g. 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 across multiple teams and synthesize their needs into software interfaces and processes.