Vice President ML Ops Engineer

Overview

Lead the design, implementation and governance of enterprise-grade MLOps frameworks that enable scalable, secure and efficient deployment of AI and Generative AI models, and mentor a high-performing ML Ops engineering team.

What you'll do

• Lead and mentor a high-performing ML Ops engineering team, driving ownership, skill development and cross-functional collaboration across Data Science, Engineering, Cloud and Security.
• Architect and govern scalable, secure and cost-optimized AWS platforms (SageMaker, EKS, Lambda, Step Functions) for enterprise ML and GenAI workloads.
• Design and implement automated ML pipelines covering data ingestion, management, feature processing, and high-throughput model training and inference workflows, ensuring reliable, low-latency, production-grade consumption patterns.
• Establish robust data and model governance frameworks, including quality checks, lineage, auditability and regulatory compliance.
• Build comprehensive model observability and monitoring systems for performance, drift, data quality, latency and operational health using CloudWatch and custom dashboards.
• Drive security, reliability and operational excellence, ensuring strong IAM practices, encryption, secrets management and cloud cost optimization.
• Definition and oversight of data governance and procedures to address control and regulatory requirements, including Data Privacy.
• Definition and oversight of data analytics and insights that support the effective management of the business and drive commercial outcomes.
• Development of a team of data professionals with expertise in data analytics, data engineering, data science and other relevant disciplines.
• Analyse the bank's current data landscape, identify key data assets and gaps, and develop a roadmap for future data initiatives.
• Monitor team performance and set clear performance expectations.
• Plan resources, budgets and policies; manage and maintain policies and processes; deliver continuous improvements and escalate breaches of policies or procedures.
• Advise key stakeholders, including functional leadership teams and senior management, on functional and cross-functional areas of impact and alignment.
• Manage and mitigate risks through assessment, in support of the control and governance agenda, and demonstrate leadership and accountability for managing risk and strengthening controls.

What you'll need

• Experience in programming and automation: Python, Bash, SQL.
• MLOps tools: MLflow, Kubeflow, AWS SageMaker Pipelines.
• Cloud platforms: AWS (SageMaker, Bedrock, Lambda, Step Functions, CloudWatch).
• DevOps expertise: CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes.
• Data management: enterprise data governance, ETL processes.
• Leadership skills: strategic planning, team management, stakeholder communication.
• Essential skills assessed may include risk and controls, change and transformation, business acumen, strategic thinking, and digital and technology.

Details

• Location: Noida.
• Flexible working with availability during select hours that overlap with US-based partners and stakeholders.