Senior Azure ML Infrastructure Engineer

Simpson Thacher & Bartlett LLP

. Lead the architecture and implementation of production-grade ML infrastructure using Azure Machine Learning, AKS, Azure Data Lake, Azure Databricks, and related services .

Posted 9/18/2026full-timeNew York City • New York • United StatesSenior💰 $160,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing production-grade ML infrastructure using Azure services, with a strong focus on MLOps best practices and automation of ML workflows. Capable of mentoring teams and ensuring compliance and security in cloud environments.

Highest-signal resume keywords

Azure Machine LearningMLOps Best PracticesDocker and KubernetesPython and Scripting LanguagesAzure DevOps

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML InfrastructureCloud EngineeringCI/CD for ML PipelinesDeep LearningAzure Data LakeAzure DatabricksAzure Active DirectoryTerraformBicepModel Monitoring Frameworks

Soft Skills

MentoringCollaboration

Tools & Technologies

MLFlowAzure ML PipelinesKubeflowAzure MonitorPrometheusGrafana

Certifications & Qualifications

Microsoft Azure Certifications

Industry Keywords

Regulated IndustriesLegal IT Experience

Tech Stack

Tools & technologies

AzureCloudDockerGrafanaJenkinsKubernetesPrometheusPythonTerraform

About the role

Key responsibilities & impact

Lead the architecture and implementation of production-grade ML infrastructure using Azure Machine Learning, AKS, Azure Data Lake, Azure Databricks, and related services
Design scalable training and inference environments for deep learning and traditional ML workloads, optimizing performance and cost
Define and implement MLOps best practices, including versioning, CI/CD for ML pipelines, monitoring, and model governance
Automate end-to-end ML workflows using MLFlow, Azure ML Pipelines, or Kubeflow
Build reusable templates and frameworks to standardize ML deployment across teams
Collaborate with data scientists to productionize models and guide infrastructure, deployment strategies, and performance optimization
Partner with DevOps and platform engineering teams to align infrastructure with broader cloud strategies and compliance standards
Mentor junior ML and platform engineers
Implement enterprise-grade security and compliance controls using Azure Active Directory, RBAC, and data encryption strategies
Integrate observability tooling such as Azure Monitor, Prometheus, and Grafana
Ensure ML systems are highly available, reliable, and scalable for production workloads

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience in lieu of formal education
5+ years of experience in ML infrastructure, cloud engineering, or MLOps
2+ years of experience working in Azure environments
Deep hands-on experience with Azure Machine Learning, AKS, Blob Storage, Databricks, Azure Data Factory, and Synapse
Strong expertise in Docker and Kubernetes, preferably AKS
Proficiency in Python and scripting languages such as Bash and PowerShell
Advanced knowledge of Azure DevOps, GitHub Actions, or Jenkins for ML workloads
Solid understanding of Terraform, Bicep, or ARM templates
Legal IT experience is a plus but not required
Microsoft Azure certifications are preferred
Experience designing ML infrastructure in regulated industries is preferred
Familiarity with feature stores, distributed training, and model monitoring frameworks is preferred
Leadership experience in building infrastructure for ML at scale is preferred
Must be able to work without employer-sponsored work visa

Benefits

Comp & perks

Competitive salary
Equity opportunities
Comprehensive benefits
Continuous learning budget
Azure certification support
Hybrid work arrangement