Senior Azure ML Infrastructure Engineer at Simpson Thacher & Bartlett

Job description

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 DevOps teams. You’ll be instrumental in scaling our machine learning capabilities from experimentation to production across multiple use cases. ESSENTIAL JOB DUTIES & RESPONSIBILITIES Infrastructure Architecture & Engineering 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. MLOps Strategy & Execution 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. Cross-Functional Leadership Collaborate with data scientists to productionize models, offering guidance on 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, sharing best practices and driving engineering excellence. Security, Reliability, and Observability 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. EDUCATION 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 SKILLS AND EXPERIENCE 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, PowerShell). Advanced knowledge of CI/CD tools such as Azure DevOps, GitHub 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 DevOps 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. WHY YOU WILL LOVE THIS ROLE 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. Salary Information NY Only: The estimated base salary range for this position is $160,000 to $180,000 at the time of posting. The actual salary offered will depend on a variety of factors, including without limitation, the qualifications of the individual applicant for the position, years of relevant experience, level of education attained, certifications or other professional licenses held, and if applicable, the location in which the applicant lives and/or from which they will be performing the job. This role is exempt meaning it is not overtime pay eligible. Simpson Thacher will not sponsor applicants for work visas for this position. Privacy Notice For information about how Simpson Thacher & Bartlett LLP collects and processes your personal information, please refer to our Privacy Notice available at stblaw.com . Simpson Thacher & Bartlett is committed to a collegial work environment in which all individuals are treated with respect and dignity. The Firm prohibits discrimination or harassment based upon race, color, religion, gender, gender identity or expression, age, national origin, citizenship status, disability, marital or partnership status, sexual orientation, veteran’s status or any other legally protected status. This Policy pertains to every aspect of an individual’s relationship with the Firm, including but not limited to recruitment, hiring, compensation, benefits, training and development, promotion, transfer, discipline, termination, and all other privileges, terms and conditions of employment. #LI-Hybrid

Requirements

Must-haves

Infrastructure Architecture & Engineering
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.
MLOps Strategy & Execution
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.
Cross-Functional Leadership
Collaborate with data scientists to productionize models, offering guidance on 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, sharing best practices and driving engineering excellence.
Security, Reliability, and Observability
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.
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.

Nice-to-haves

Legal IT experience a plus but not required
Microsoft Azure certifications (e.g., Azure AI Engineer Associate, Azure Solutions Architect Expert, or DevOps 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.

Application prep

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Keywords to mirror

azuremlmachine learningci/cddockerkubernetespythonai

Platform: workday
Effort: long

Benefits

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Learning Budget