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About the Role
Lead the design, development, and optimization of scalable ML infrastructure on Microsoft Azure. You will be the technical lead for deploying and maintaining robust ML Ops frameworks, ensuring efficient collaboration between data science, engineering, and DevOps teams, and scaling machine learning capabilities from experimentation to production.
Responsibilities
- 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 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.
- 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.
Requirements
- 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.
- 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.
Skills
- Azure Machine Learning
- AKS
- Azure Data Lake
- Azure Databricks
- MLOps
- MLFlow
- Azure ML Pipelines
- Kubeflow
- Azure Active Directory
- RBAC
- Azure Monitor
- Prometheus
- Grafana
- Docker
- Kubernetes
- Python
- Bash
- PowerShell
- Azure DevOps
- GitHub Actions
- Jenkins
- Terraform
- Bicep
- ARM templates
Location
- NY Only
Work Type
- Hybrid
Experience Level
- Senior
- 5+ years of experience in ML infrastructure, cloud engineering, or MLOps
- 2+ years of experience working in Azure environments.
Education Level
- Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience
Salary/Compensations
- $160,000 to $180,000
Benefits
- Competitive salary
- Equity opportunities
- Comprehensive benefits
- Continuous learning budget
- Azure certification support
About the Company
- Simpson Thacher & Bartlett LLP is committed to a collegial work environment in which all individuals are treated with respect and dignity.
Equal Opportunity
- 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.