MLops Platform Engineer

Job Description:

Role Overview

As a Senior Platform Engineer – MLOps, you will play a key role in designing, building, and

maintaining the infrastructure and workflows that support our machine learning systems. You’ll

work closely with data scientists, ML engineers, and DevOps teams to implement scalable

MLOps solutions, mentor team members, and ensure smooth deployment and operation of ML

models in production environments.

Key Responsibilities

• Design and implement end-to-end MLOps pipelines for CI/CD, model versioning,

automated testing, and data workflows.

• Architect and manage scalable infrastructure using cloud platforms (AWS/GCP/Azure)

with Kubernetes and Docker.

• Deploy, monitor, and maintain production-grade ML models, ensuring performance,

reliability, and compliance.

• Automate ML workflows, including data ingestion, model training, testing, deployment,

and monitoring.

• Collaborate with cross-functional teams to integrate ML solutions into broader platform

ecosystems.

• Mentor junior DevOps and MLOps engineers, promoting best practices and a strong

DevOps culture.

• Stay up to date with the latest trends and tools in MLOps to continuously improve the

platform.

Required Skills & Experience

• 5+ years of experience in platform engineering with a strong focus on MLOps.

• Hands-on expertise in building and maintaining CI/CD pipelines for ML models.

• Experience with MLOps tools such as Kubeflow, MLflow, Airflow, etc.

• Proficiency in cloud platforms: AWS, GCP, or Azure.

• Strong knowledge of containerization and orchestration: Docker and Kubernetes.

• Programming  experience in Python (Go is a plus) for scripting and automation.
• Solid understanding of model lifecycle management: training, validation, deployment,
and monitoring.
• Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana).
• Excellent problem-solving and collaboration skills, with experience in team leadership or
mentoring.