ML Engineer

KPI Partners; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

KPI Partners, based in Bengaluru, seeks a Senior Machine Learning Engineer to drive ML and MLOps initiatives across production environments. You will own deployed models, design scalable pipelines, and collaborate with data scientists to translate business problems into reliable solutions.

The role emphasizes Azure Machine Learning, Python-based development, and containerized deployments, with opportunities to influence architecture and operational best practices in a fast-growing team.

Qualifications

• Extensive ML engineering / MLOps with production ownership of deployed models.
• Strong expertise in Azure Machine Learning including workspace, compute, environments and model registry.
• Hands-on with Azure ML SDK/CLI for jobs, pipelines and deployment.
• Proficient in Python for production-grade code and packaging.
• Experience with Docker, IaC (Terraform) and containerised deployments.
• Familiarity with Apache Airflow and CI/CD for ML workloads.
• Model monitoring, drift detection and alerting in production.

Skills

ML engineering

MLOps

Python

CI/CD

Model monitoring

Drift detection

Production ownership

Tools

Azure Machine Learning

Azure ML SDK

Azure ML CLI

Docker

Terraform

Apache Airflow

GitHub Actions

Job description

KPI Partners is seeking a talented and motivated Senior Machine Learning Engineer to join our dynamic team. In this role, you will leverage your expertise in machine learning and data science to develop innovative solutions that drive business insights and enhance operational efficiency.

Job Title: ML Engineer

Experience: 6+ years

Employment Type: Full-time

Experience required

• Extensive knowledge in ML engineering / MLOps, including production ownership of deployed models.
• Strong, demonstrable expertise in Azure Machine Learning —workspace administration, compute management, environments, datastores and data assets, model registry, and online and batch endpoint deployment.
• Hands-on expertise with the Azure ML Python SDK and the Azure ML CLI, including authoring jobs, components, pipelines and deployment YAML programmatically.
• Working knowledge of the wider Azure ecosystem: Azure Storage (Blob /ADLS Gen2), Key Vault, Container Registry, Entra ID and managed identities, Azure Monitor and Application Insights, GitHub Actions for CI/CD.
• Advanced Python — modular, tested, production-grade code; packaging; comfort workingin a shared repository.
• Docker and containerised model deployment; exposure to infrastructure as code (Terraform).
• Practical experience with a workflow orchestrator — Apache Airflow preferred.
• Experience with Git-based branching, code review and CI/CD for machine learning workloads.
• Experience implementing model monitoring, drift detection and alerting in production.