Machine Learning Engineer
Barclays · Pune · Posted 2026-07-18
Tech stack: AWS, Docker, Terraform
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About 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.
Responsibilities:
- 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.
Qualifications:
- 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.
- 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 in Pune.
Qualifications
- 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.
- 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 in Pune.
Responsibilities
- 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.