Senior Azure ML Infrastructure Engineer

Simpson Thacher & Bartlett LLP is looking for a Senior Azure ML Infrastructure Engineer to lead the design, development, and optimization of scalable ML infrastructure on Microsoft Azure. In this role, you will be the technical lead for deploying and maintaining robust (Machine Learning) ML Ops frameworks, ensuring efficient collaboration between data science, engineering, and Dev Ops teams. You’ll be instrumental in scaling our machine learning capabilities from experimentation to production across multiple use cases.

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: versioning, CI/CD for ML pipelines, monitoring, and model governance. Automate end-to-end ML workflows using tools such as MLFlow, Azure ML Pipelines, or Kubeflow. Build reusable templates and frameworks to standardize ML deployment across teams.

Collaborate with data scientists to product ionize models, offering guidance on infrastructure, deployment strategies, and performance optimization. Partner with Dev Ops and platform engineering teams to align infrastructure with broader cloud strategies and compliance standards. Mentor junior ML and platform engineers, sharing best practices and driving engineering excellence.

Implement enterprise-grade security and compliance controls using Azure Active Directory, RBAC, and data encryption strategies. Integrate observability tooling (e.g., Azure Monitor, Prometheus, Grafana) for end-to-end monitoring of ML systems. Ensure systems are highly available, reliable, and scalable to meet the demands of production ML workloads.

Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience in lieu of formal education. Legal IT experience a plus but not required.

Required

• 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 cloud services relevant to ML, including Azure Machine Learning, AKS, Blob Storage, Databricks, Azure Data Factory, and Synapse.
• Strong expertise in containerization (Docker) and orchestration (Kubernetes, preferably AKS).
• Proficient in Python and scripting languages (e.g., Bash, Power Shell).
• Advanced knowledge of CI/CD tools such as Azure Dev Ops, Git Hub Actions, or Jenkins for ML workloads.
• Solid understanding of IaC tools:
  Terraform, Bicep, or ARM templates.

Preferred

• Microsoft Azure certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, or Dev Ops Engineer Expert).
• Experience designing ML infrastructure in regulated industries (finance, healthcare, etc.).
• Familiarity with feature stores, distributed training, and model monitoring frameworks.
• Leadership experience in building infrastructure for ML at scale.

Lead the development of high-impact ML platforms that support real-world AI applications. Influence the direction of our ML and cloud infrastructure strategy. Work in a forward-thinking, collaborative team that values experimentation, clean architecture, and automation.

Competitive salary, equity opportunities, and comprehensive benefits.

Continuous learning budget and Azure certification support.

The estimated base salary range for this…