MLOps Engineer

Purpose of the role

To implement data quality process and procedures, ensuring that data is reliable and trustworthy, then extract actionable insights from it to help the organisation improve its operation, and optimise resources. 

Accountabilities

• Investigation and analysis of data issues related to quality, lineage, controls, and authoritative source identification.
• Execution of data cleansing and transformation tasks to prepare data for analysis.
• Designing and building data pipelines to automate data movement and processing.
• Development and application of advanced analytical techniques, including machine learning and AI, to solve complex business problems.
• Documentation of data quality findings and recommendations for improvement.

Assistant Vice President Expectations

• To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
• Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes
• 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 lead collaborative assignments and guide team members through structured assignments, identify the need for the inclusion of other areas of specialisation to complete assignments. They will identify new directions for assignments and/ or projects, identifying a combination of cross functional methodologies or practices to meet required outcomes.
• Consult on complex issues; providing advice to People Leaders to support the resolution of escalated issues.
• Identify ways to mitigate risk and developing new policies/procedures in support of the control and governance agenda.
• Take ownership for managing risk and strengthening controls in relation to the work done.
• Perform work that is closely related to that of other areas, which requires understanding of how areas coordinate and contribute to the achievement of the objectives of the organisation sub-function.
• Collaborate with other areas of work, for business aligned support areas to keep up to speed with business activity and the business strategy.
• Engage in complex analysis of data from multiple sources of information, internal and external sources such as procedures and practises (in other areas, teams, companies, solve problems creatively and effectively.
• Communicate complex information. 'Complex' information could include sensitive information or information that is difficult to communicate because of its content or its audience.
• Influence or convince stakeholders to achieve outcomes.

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 ML Operations 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 design, implement, and maintain robust MLOps frameworks that streamline the deployment, monitoring, and lifecycle management of AI and Generative AI models, ensuring efficient and reliable production operations on AWS.

Responsibilities of the role:

• Build and optimize scalable, secure, and cost-effective AWS-based infrastructure for ML/GenAI workloads

• Develop automated workflows for data ingestion, model training, testing, deployment, and monitoring using tools like AWS SageMaker, Step Functions, and Lambda

• Implement data quality checks, lineage tracking, and compliance standards for curated datasets

• Integrate ML pipelines with DevOps practices, ensuring seamless collaboration between data science and engineering teams

• Deploy monitoring solutions for model performance, drift detection, and system health using AWS CloudWatch and custom dashboards

• Ensure adherence to security best practices and governance requirements for AI deployments

• Work closely with Data Scientists to operationalize models and optimize deployment strategies

Technical skills required for this role include: 

• Experience in Programming & Automation: Python, Bash, SQL.

• Worked in MLOps Tools: MLflow, Kubeflow, AWS SageMaker Pipelines.

• Cloud Platforms: AWS (SageMaker, Bedrock, Lambda, Step Functions, CloudWatch)

• DevOps: CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes

• Data Management: Data curation, governance, and ETL processes.

The ML Ops Engineer role focuses on building and managing automated pipelines, AWS-based architectures, and monitoring frameworks to enable efficient deployment and lifecycle management of AI and Generative AI models in production environments.

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.