✨

Vista previa generada por IA

Join Barclays as a Machine Learning Engineer in Pune, driving scalable AI solutions.

Lead end-to-end ML lifecycle, from data pipelines to governance, in a regulated environment.

Purpose of the role

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure.

Accountabilities

Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.

Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.

Development of processing and analysis algorithms fit for the intended data complexity and volumes.

Collaboration with data scientist to build and deploy machine learning models.

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 escalated 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.

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

Advise key stakeholders, including functional leadership teams and senior management on functional and cross functional areas of impact and alignment.

Manage and mitigate risks

Manage and mitigate risks through assessment, in support of the control and governance agenda.

Demonstrate leadership and accountability

Demonstrate leadership and accountability for managing risk and strengthening controls in relation to the work your team does.

Demonstrate comprehensive understanding

Demonstrate comprehensive understanding of the organisation functions to contribute to achieving the goals of the business.

Collaborate with other areas of work

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

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

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.

All colleagues will be expected to demonstrate the Barclays Values

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.

Barclays is looking for a Machine Learning Engineer

Barclays is looking for a Machine Learning Engineer to own and lead the design, build and operation of scalable, reliable and governed machine learning systems in production. You will work closely with Data Scientists to bridge experimentation and production, enabling faster, safer and reproducible delivery of ML models in governed environments.

You will own the ML lifecycle including infrastructure, deployment, monitoring, retraining and governance, while shaping technical strategy and organisational MLOps maturity.

To be a successful Machine Learning Engineer

To be a successful Machine Learning Engineer, you should have experience with:

Proven track record of deploying and operating machine learning models in production within governed or regulated environments. Strong software engineering background with experience delivering large scale systems.
Experience using cloud ML platforms such as Databricks, AWS SageMaker or equivalent. Strong practical understanding of machine learning algorithms and statistical methods with the ability to evaluate model behaviour, performance and risk in production.
Experience building and operating production grade ML platforms or large scale data platforms. Proficiency with Docker and CI/CD tooling.
Strong understanding of distributed systems, scalable architectures and API based services. Ability to balance experimentation velocity with operational reliability, risk management and governance expectations.
Experience with MLflow, feature stores and model registry implementations. Hands on experience with data validation, drift detection and ML observability tooling.
Infrastructure as Code using Terraform or CloudFormation. Experience working in financial services or other highly regulated industries.
Experience with responsible deployment of Generative AI systems in production environments. Prior ownership of enterprise ML platforms or MLOps standards.

Machine Learning Engineer