MLOps Engineer

Job Title: RunOps Support MLOps

Job Type: Full-Time

Location: Hybrid Hyderabad, Pune, New Delhi

About Us

At micro1 we're building the world's first AI recruitment engine to match the world's most talented people with their dream jobs. As a core team member, you'll be at the forefront of AI innovation, contributing to our company's expansion while collaborating with Silicon Valley's fastest-growing companies. You'll thrive in a supportive, fast-paced, and innovative environment, tackling exciting challenges while unlocking unparalleled career growth and development.

Job Summary:

We are seeking a proactive and skilled RunOps Support Engineer specializing in ML Ops for remote work. In this role, you will be responsible for the seamless deployment, monitoring, and maintenance of machine learning models in production environments. Your expertise will facilitate the automation of workflows and ensure the reliability of ML pipelines, addressing runtime issues and optimizing performance.

Key Responsibilities:

Monitor model serving systems and address performance or latency issues.

Ensure ML pipelines run as scheduled and scale appropriately.

Support model retraining, deployment, and rollback processes.

Collaborate with Data Scientists and ML Engineers on productionizing ML workflows.

Maintain observability tools and logs for model health tracking.

Assist in automating workflows and handling model versioning.

Resolve runtime issues across ML pipelines.

Required Skills and Qualifications:

2+ years in ML Ops or platform operations for ML systems.

Familiarity with model deployment tools such as MLflow, Seldon, SageMaker, or Azure ML.

Experience with Docker, Kubernetes, and monitoring ML workflows.

Understanding of CI/CD pipelines in the ML lifecycle.

Excellent written and verbal communication skills.

Strong problem-solving skills with attention to detail.

Preferred Qualifications:

Experience with Vertex AI or similar platforms.

Background in software engineering or data science.

Proven track record in a remote or hybrid work environment.