Vice President ML Ops Engineer

Purpose of the role

To develop and lead the execution of business unit/function data strategy, ensuring the effective and efficient use of data to drive informed decision-making, support regulatory and control requirements, improve operational efficiency, and generate business value. 

Accountabilities

• 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 well as driving commercial outcomes.
• Development of a team of data professionals with expertise in data analytics, data engineering, data science, and other relevant disciplines.
• Analysis of the bank's current data landscape, identify key data assets and gaps, and develop a roadmap for future data initiatives.
• Monitoring team performance and setting clear performance expectations.

Vice President Expectations

• To contribute or set strategy, drive requirements and make recommendations for change. Plan resources, budgets, and policies; manage and maintain policies/ processes; deliver continuous improvements and escalate breaches of policies/procedures..
• If managing a team, they define jobs and responsibilities, planning for the department’s future needs and operations, counselling employees on performance and contributing to employee pay decisions/changes. They may also lead a number of specialists to influence the operations of a department, in alignment with strategic as well as tactical priorities, while balancing short and long term goals and ensuring that budgets and schedules meet corporate requirements..
• If the position has leadership responsibilities, People Leaders are expected to demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard. The four LEAD behaviours are: L – Listen and be authentic, E – Energise and inspire, A – Align across the enterprise, D – Develop others..
• OR for an individual contributor, they will be a subject matter expert within own discipline and will guide technical direction. They will lead collaborative, multi-year assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will train, guide and coach less experienced specialists and provide information affecting long term profits, organisational risks and strategic decisions..
• 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.
• Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.
• Demonstrate comprehensive understanding of the organisation functions to contribute to achieving the goals of the business.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategies.
• Create solutions based on sophisticated analytical thought comparing and selecting complex alternatives. In-depth analysis with interpretative thinking will be required to define problems and develop innovative solutions.
• Adopt and include the outcomes of extensive research in problem solving processes.
• Seek out, build and maintain trusting relationships and partnerships with internal and external stakeholders in order to accomplish key business objectives, using influencing and negotiating skills to achieve outcomes.
• The role is responsible for leading the design and governance of MLOps frameworks, AWS-based architectures, and automation strategies to enable efficient, secure, and scalable deployment of AI and Generative AI models

All colleagues will be expected to demonstrate the Barclays Values of Respect, Integrity, Service, Excellence and Stewardship – our moral compass, helping us do what we believe is right. They will also be expected to demonstrate the Barclays Mindset – to Empower, Challenge and Drive – the operating manual for how we behave.

Embark on a transformative journey as Vice President-ML Ops Engineer at Barclays, where you will play a pivotal role to manage operations within a business area and maintain processes with risk management initiatives. You will take ownership of your work and provide first-class support to our clients with expertise and care.

Purpose of the role:

To lead the design, implementation, and governance of enterprise-grade ML Ops frameworks that enable scalable, secure, and efficient deployment of AI and Generative AI models, while driving best practices in automation, monitoring, and AWS architecture.

Accountabilities are given below:

• 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/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.

Technical skills required for this role include:

• Experience in Programming & 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

The VP-MLOps Engineer role is responsible for leading the design and governance of MLOps frameworks, AWS-based architectures, and automation strategies to enable efficient, secure, and scalable deployment of AI and Generative AI models.

This role requires a flexible working approach, ensuring availability during select hours that overlap with US-based partners and stakeholders.

You may be assessed on key essential skills relevant to succeed in role, such as risk and controls, change and transformation, business acumen, strategic thinking and digital and technology, as well as job-specific  technical skills.

This role is based out of Noida.