ML Platform Engineer

enter; Bengaluru; On-site; INR 900,000 - 1,200,000; Full time

Job summary

enter is seeking an ML infrastructure engineer in Bengaluru to build and maintain scalable systems that empower ML teams to train, deploy, and monitor models end-to-end. You will develop automation, design robust versioning and artifact strategies, and implement CI/CD pipelines tailored for ML workloads.

The role requires hands-on experience with Docker, Kubernetes, and major cloud platforms, along with experience in ML pipelines from data to production observability.

Qualifications

• Strong programming ability and solid problem-solving mindset.
• Hands-on knowledge of ML pipelines from training through deployment to monitoring.
• Practical experience with Docker, Kubernetes, CI/CD workflows, and major cloud platforms.
• Familiarity with model versioning systems and infrastructure patterns for scaling ML workloads.

Responsibilities

• Build and maintain infrastructure that enables ML teams to train, deploy, and monitor models at scale.
• Develop automation and tooling around ML workflows to reduce friction in the model lifecycle.
• Design systems for model versioning, artifact management, and reproducible experiments.
• Work on CI/CD pipelines tailored to machine learning workloads and requirements.
• Docker and Kubernetes for containerization and orchestration of ML applications.

Skills

Programming ability

Problem solving

Docker

Kubernetes

CI/CD

Cloud platforms

Tools

Docker

Kubernetes

CI/CD

Cloud platforms

Job description

Batch: 2019/2020/2021/2022/2023/2024.

About the role

• Build and maintain infrastructure that enables machine learning teams to train, deploy, and monitor models at scale
• Develop automation and tooling around ML workflows to reduce friction in the model lifecycle
• Design systems for model versioning, artifact management, and reproducible experiments
• Work on CI/CD pipelines tailored to machine learning workloads and requirements

What you'll work on

• Docker and Kubernetes for containerization and orchestration of ML applications
• Cloud platform services to support model training, inference, and data pipelines
• MLOps tools and practices that help teams move models from experimentation to production
• Monitoring and observability systems that track model performance and system health

What we're looking for

• Strong programming ability and solid problem-solving mindset
• Hands-on knowledge of machine learning pipelines from training through deployment to monitoring
• Practical experience with Docker, Kubernetes, CI/CD workflows, and major cloud platforms
• Familiarity with model versioning systems and infrastructure patterns for scaling machine learning workloads