MLOps Infrastructure Engineer – AI Security

Location: Bengaluru India
Function: AI Security - Technology & Innovation Centre (TIC)
Employment Type: Full-time

The MLOps / Infrastructure Engineer - AI Security is responsible for building operating and scaling the infrastructure and ML pipelines that support AI security research and production systems. The role ensures that AI models datasets experiments and security evaluations are reproducible observable and deployable in a secure and compliant manner.

- Design build and maintain ML pipelines for model training evaluation and AI security testing.

- Own infrastructure for experimentation benchmarking and large-scale model analysis.

- Integrate AI security checks (e.g. scanning verification unlearning workflows) into ML pipelines.

- Manage compute storage and orchestration environments for research and platform workloads.

- Ensure reproducibility traceability and auditability of models data and experiments.

- Support deployment monitoring and lifecycle management of AI security tools and services.

- Collaborate with research backend and security teams to enable end-to-end delivery.

- Strong experience with MLOps platforms ML pipelines and workflow orchestration tools.
- Hands-on experience with containerisation and orchestration (Docker Kubernetes or equivalent).
- Experience managing cloud or on-prem compute environments for ML workloads.
- Familiarity with experiment tracking model registries and data versioning tools.
- Understanding of security compliance and access control considerations in ML systems.
- Strong scripting and programming skills (Python preferred).

Education:
- Bachelors or Masters degree in Computer Science Engineering or a related field.

Experience:
- 5-8 years of experience in MLOps ML infrastructure or platform engineering roles.
- Proven experience supporting ML research or production ML systems at scale.

- Reliable scalable infrastructure enabling AI security research and platform development.
- High reproducibility and observability across ML experiments and security evaluations.
- Smooth deployment and operation of AI security pipelines and services.

Required Skills:

MLPythonDockerKubernetes