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Senior Software Engineer - ML Ops

Senior Software Engineer - ML Ops

Stryker

5 - 10 years

4 - 10 Lacs

bengaluru

Posted:1 week ago| Platform: GlassDoor logo

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Skills Required

software ml flexibility design model training testing deployment data azure databricks devops github jenkins automate provisioning terraform mlflow versioning tracking management security compliance optimization monitoring drift programming python sql docker kubernetes storage analytics logging preprocessing service retrieval certifications ai

Work Mode

On-site

Job Type

Part Time

Job Description

Work Flexibility: Hybrid

What You Will Do:

Design, build, and maintain end-to-end MLOps pipelines for ML model training, testing, and deployment.

Collaborate with Data Scientists to productionize ML models in Azure ML and Azure Databricks.

Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.

Automate infrastructure provisioning using IaC tools (Terraform, ARM templates, or Bicep).

Monitor and manage deployed models using Azure Monitor, Application Insights, and MLflow.

Implement best practices in model versioning, model registry, experiment tracking, and artifact management.

Ensure security, compliance, and cost optimization of ML solutions deployed on Azure.

Work with cross-functional teams (Data Engineers, DevOps Engineers, Data Scientists) to streamline ML delivery.

Develop monitoring/alerting for ML model drift, data drift, and performance degradation.

What You Need:

Required Skills
- 5-10 years of experience in programming: Python (must), SQL;. MLOps/DevOps Tools: MLflow, Azure DevOps, GitHub Actions, Docker, Kubernetes (AKS).
- Azure Services: Azure ML, Azure Databricks, Azure Data Factory, Azure Storage, Azure Functions, Azure Event Hubs.
- CI/CD: Experience designing pipelines for ML workflows. IaC: Terraform, ARM templates, or Bicep.
- Data Handling: Experience with Azure Data Lake, Blob Storage, and Synapse Analytics.
- Monitoring & Logging: Azure Monitor, Prometheus/Grafana, Application Insights. Strong knowledge of ML lifecycle (data preprocessing, model training, deployment, monitoring).
Preferred Skills:
- Experience with Azure Kubernetes Service (AKS) for scalable model deployment.
- Knowledge of feature stores and distributed training frameworks. Familiarity with RAG (Retrieval Augmented Generation) pipelines and LLMOps.
- Azure certifications such as Azure AI Engineer Associate, Azure Data Scientist Associate, or Azure DevOps Engineer Expert.

Travel Percentage: 10%

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