Sr. Python MLOps Engineer – Modeling Automation & ML Pipelines | GCP/Azure ML | 7+ Years | Bang[...]

UST; Bengaluru; On-site; INR 1,000,000 - 2,000,000; Full time

Job summary

UST is seeking candidates in Bengaluru, India for a role focused on Python Development and MLOps Engineering. The ideal candidate should have over 7 years of experience with strong expertise in modeling automation, CI/CD pipelines, and cloud platforms like GCP or Azure.

Candidates should be prepared to handle model training pipelines, workflow orchestration, and have strong debugging skills. Interested individuals can apply via email.

Qualifications

• 7+ years of experience in Python Development and MLOps Engineering.
• Strong experience in Modeling Automation and ML pipeline development.
• Proficiency in Docker, Kubernetes, and cloud-native deployments.

Skills

Python Development

MLOps Engineering

Modeling Automation

ML pipeline development

GCP Vertex AI

Azure ML

Docker

Kubernetes

CI/CD pipelines

Git

Tools

MLflow

Kubeflow

Airflow

Prefect

Job description

Candidates ready to join immediately can share their details via email for quick processing.

[e-mail]

Must-Have Skills

• 7+ years of experience in Python Development and MLOps Engineering
• Strong experience in Modeling Automation and ML pipeline development
• Expertise in MLOps frameworks and model lifecycle management
• Hands-on experience with model training pipelines and workflow orchestration
• Experience in artifact versioning and model registry management
• Strong knowledge of GCP Vertex AI or Azure ML
• Experience with CI/CD pipelines for ML deployments
• Proficiency in Docker, Kubernetes, and cloud-native deployments
• Strong scripting, automation, and debugging skills
• Experience with Git, ML experiment tracking, and monitoring tools

Good-to-Have Skills

• Experience with MLflow, Kubeflow, Airflow, or Prefect
• Exposure to LLMOps / Generative AI deployment workflows
• Knowledge of Terraform or Infrastructure as Code (IaC)
• Familiarity with Databricks or Spark-based ML workloads
• Experience with feature stores and data versioning
• Exposure to AWS SageMaker
• Strong understanding of scalable AI platform architecture