ML Engineer (Azure ML / MLOps)

ML Engineer (Azure ML / MLOps) – Remote Opportunity

Position: ML Engineer (Azure ML / MLOps)

Client: Coforge

Duration: 6–12 Months (Contract)

Location: 100% Remote

Working Hours: India Day Shift

Important: Candidates should be available and responsive on WhatsApp during Indian working hours.

Please share your updated resume along with your LinkedIn profile link. Also mention your notice period / how soon you can join if selected for the project.

Mandatory Skills

Azure Machine Learning Studio

MLOps Engineering (MLE)

MLflow

Azure Kubernetes Service (AKS) Clusters

Azure DevOps Pipelines

Experience Required

️ 6–8 Years

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions using Azure ML Studio.
• Register, manage, and deploy ML models within Azure ML environments.
• Build scalable training, inference, monitoring, and data processing pipelines.
• Deploy and manage ML workloads on AKS clusters.
• Implement enterprise-grade MLOps workflows and best practices.
• Develop CI/CD pipelines using Git and Azure DevOps.
• Manage model lifecycle using MLflow (tracking, registry, logging, monitoring).
• Monitor model performance, drift detection, and operational reliability.
• Collaborate with Data Scientists, ML Engineers, and DevOps teams.
• Maintain code quality using Python best practices, Black, Flake8, and automation tools.
• Support data engineering workflows using Azure Data Factory and Blob Storage.
• Troubleshoot deployment, performance, and operational issues.

Required Skills & Experience

• Hands-on expertise with:
• Azure Machine Learning Studio
• Azure Kubernetes Service (AKS)
• Azure Blob Storage
• Azure Data Factory (ADF)
• Azure DevOps (ADO) Pipelines
• Experience deploying ML/AI/GenAI solutions in enterprise environments.
• Strong Python programming and automation skills.
• Experience with MLflow for experiment tracking and model management.
• Strong understanding of MLOps principles and CI/CD implementation.
• Experience with model monitoring, drift detection, and performance optimization.
• Basic to intermediate data engineering knowledge.

Nice to Have

⭐ Docker & containerized deployments

⭐ Distributed computing / scalable AI infrastructure

⭐ Cloud-native security and governance practices for AI/ML platforms

Interested candidates, please share:

1. Updated Resume
2. LinkedIn Profile Link
3. Total Experience
4. Relevant Azure ML/MLOps Experience
5. Current CTC
6. Expected CTC
7. Notice Period
8. Earliest Joining Date / How soon you can join

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