Machine Learning Engineer - Synapse Analytics

We are seeking a highly skilled Microsoft Azure Machine Learning professional with 510 years of experience to design, build, deploy, and manage scalable machine learning solutions on the Azure cloud platform.

The ideal candidate will have strong hands-on expertise in Azure Machine Learning (Azure ML), MLOps, data engineering, and model lifecycle management, and will collaborate closely with data scientists, cloud architects, and business stakeholders to deliver production-grade ML solutions.

Key Responsibilities :

Azure ML & Model Development :

- Design, develop, train, evaluate, and deploy machine learning models using Azure Machine Learning Studio and SDKs

- Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, validation, and inference

- Implement automated training workflows and experiment tracking using Azure ML experiments and runs

- Optimize models for performance, scalability, and cost efficiency

MLOps & Deployment :

- Implement MLOps practices using Azure ML Pipelines, Azure DevOps, GitHub Actions, and CI/CD workflows

- Deploy models using AKS, Azure Container Instances (ACI), or managed online endpoints

- Monitor deployed models for drift, accuracy, latency, and performance degradation

- Manage versioning of models, datasets, and environments

Data Engineering & Integration :

- Work with large-scale structured and unstructured datasets from Azure Data Lake, Blob Storage, Azure SQL, Synapse Analytics

- Collaborate with data engineering teams to build robust data pipelines using Azure Data Factory

- Ensure data quality, governance, and compliance with security standards

Cloud Architecture & Security :

- Design secure and scalable ML solutions leveraging Azure networking, identity (AAD), RBAC, Key Vault

- Implement best practices for cost optimization, monitoring, and logging using Azure

Monitor and Application :

- Insights Ensure compliance with enterprise security and governance policies

Collaboration & Stakeholder Engagement :

- Partner with data scientists to operationalize ML models for production

- Work with product owners and business teams to translate requirements into ML solutions Mentor junior engineers and contribute to best practices and technical standards

- Participate in code reviews, architecture discussions, and technical documentation

Required Skills & Qualifications :

Technical Skills :

- 5 - 10 years of experience in Machine Learning, Data Science, or Cloud Engineering Strong hands-on experience with Microsoft Azure Machine Learning

- Proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch preferred)

- Experience with Azure ML SDK (v1/v2), ML pipelines, experiments, and endpoints

- Hands-on experience with MLOps, CI/CD pipelines, and model lifecycle management

- Knowledge of Docker and Kubernetes (AKS) Experience working with Azure Data services (Data Lake, Blob Storage, Synapse, SQL)

- Familiarity with REST APIs and model serving frameworks Cloud & DevOps

- Experience with Azure DevOps / GitHub Actions Infrastructure as Code exposure using ARM templates, Bicep, or Terraform

- Understanding of cloud networking, security, and identity management in Azure

Soft Skills :

- Strong analytical and problem-solving abilities

- Excellent communication and stakeholder management skills

- Ability to work independently and in cross-functional teams

- Mentoring and leadership capabilities