Description

Key Skills: MLOps, ML, Pipeline

Roles and Responsibilities:

• Operationalize machine learning workflows across the ML lifecycle, from training outputs to production usage.
• Build and maintain MLOps processes to support repeatable deployments, versioning, and reliable model updates.
• Support end-to-end ML pipelines for data/model movement and automation of recurring ML tasks.
• Troubleshoot production issues related to ML services and ensure stable model behavior in real-world conditions.
• Collaborate with ML engineers to standardize deployment practices and improve operational efficiency.

Skills Required:

• 4 - 12 years of relevant experience in ML and MLOps.
• Machine Learning (ML) experience delivering models for real use cases.
• MLOps experience operationalizing ML systems and managing production workflows.
• Pipeline experience to support automated ML delivery and repeatable execution.
• Hands-on experience with deployment and operationalization practices for ML models.

Good to Have:

• Additional pipeline exposure and experience improving pipeline reliability and throughput.

Education: Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

Key Skills

Education

Any Graduate