MLOps Engineer

Purpose of the role

To build and maintain infrastructure platforms and products that support applications and data systems, using hardware, software, networks, and cloud computing platforms as required with the aim of ensuring that the infrastructure is reliable, scalable, and secure.  Ensure the reliability, availability, and scalability of the systems, platforms, and technology through the application of software engineering techniques, automation, and best practices in incident response. 

Accountabilities

• Build Engineering:  Development, delivery, and maintenance of high-quality infrastructure solutions to fulfil business requirements ensuring measurable reliability, performance, availability, and ease of use. Including the identification of the appropriate technologies and solutions to meet business, optimisation, and resourcing requirements.
• Incident Management: Monitoring of IT infrastructure and system performance to measure, identify, address, and resolve any potential issues, vulnerabilities, or outages. Use of data to drive down mean time to resolution.
• Automation:  Development and implementation of automated tasks and processes to improve efficiency and reduce manual intervention, utilising software scripting/coding disciplines.
• Security: Implementation of a secure configuration and measures to protect infrastructure against cyber-attacks, vulnerabilities, and other security threats, including protection of hardware, software, and data from unauthorised access.
• Teamwork:  Cross-functional collaboration with product managers, architects, and other engineers to define IT Infrastructure requirements, devise solutions, and ensure seamless integration and alignment with business objectives via a data driven approach.
• Learning: Stay informed of industry technology trends and innovations, and actively contribute to the organization's technology communities to foster a culture of technical excellence and growth.

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, etc).to 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.

Join us as an MLOps Engineer at Barclays, where you will be responsible for operationalizing cutting-edge machine learning and generative AI solutions, ensuring scalable, secure, and efficient deployment across infrastructure. You will work closely with data scientists, ML engineers, and business stakeholders to build and maintain robust MLOps pipelines, enabling rapid experimentation and reliable production implementation of AI models, including LLMs and real-time analytics systems.

To be successful as an MLOps Engineer you should have experience with:

• Strong programming skills in Python and experience with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch)

• Experience with Jenkins, GitHub Actions, or GitLab CI/CD for automating ML pipelines

• Strong knowledge of Docker and Kubernetes for scalable deployments

• Deep experience with AWS services (e.g., SageMaker, Bedrock, Lambda, CloudFormation, Step Functions, S3 and IAM).

• Managing infrastructure for training and inference using AWS S3, EC2, EKS, and Step Functions.

• Experience with Infrastructure as Code (e.g., Terraform, AWS CDK)

• Familiarity with model lifecycle management tools (e.g., MLflow, SageMaker Model Registry).

• Strong understanding of DevOps principles applied to ML workflows.

Some other highly valued skills may include:

• Experience with Snowflake, Databricks for collaborative ML development and scalable data processing.

• Knowledge of data engineering tools (e.g., Apache Airflow, Kafka, Spark).

• Understanding model interpretability, responsible AI, and governance.

• Contributions to open-source MLOps tools or communities.

• Strong leadership, communication, and cross-functional collaboration skills.

• Knowledge of data privacy, model governance, and regulatory compliance in AI systems.

• Exposure to LangChain, Vector DBs (e.g., FAISS, Pinecone), and retrieval-augmented generation (RAG) pipelines.

You may be assessed on key critical skills relevant for success 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 Pune.