​​Sr. Manager/Staff Engineer, AI Infrastructure and Operations​

ROLE SUMMARY 

The Senior Manager/Staff Engineer, AI Infrastructure & MLOps Engineering is a senior individual contributor position reporting directly to the Director, AI Infrastructure and Operations Lead. This role is highly technical, hands-on, and focused on building the core automation, tooling, and infrastructure that power the internal AI platform and services. 

 In this position, the Senior Manager/Staff Engineer is responsible for designing and implementing systems that enable scientists and engineers to rapidly build, deploy, and monitor machine learning models in production. Work will span Python-based automation, containerization with Docker, CI/CD pipelines, AWS cloud infrastructure, microservices, and high-performance model serving frameworks. 

 The role plays a critical part in advancing the organization’s MLOps capabilities by creating reusable components and internal developer platforms that increase velocity, reliability, and scalability of AI/ML delivery. 

ROLE RESPONSIBILITIES  

Core Engineering & Automation 

• Design, build, and maintain Python-based tooling, SDKs, and automation frameworks to support model development, deployment, and monitoring workflows. 

• Develop containerized solutions using Docker and orchestrate them using Kubernetes (including Kubeflow or similar MLOps platforms). 

• Build and maintain CI/CD pipelines to streamline ML model integration, testing, and deployment into production environments. 

• Implement robust automation for provisioning, configuring, and managing cloud resources using Infrastructure-as-Code (Terraform, Pulumi, AWS CDK, etc.). 

Cloud Infrastructure & Platform Engineering 

• Architect and manage scalable, secure, and high-availability AWS infrastructure to support AI workloads. 

• Develop and optimize microservices architectures for AI/ML serving, ensuring high throughput and low latency. 

• Build and maintain APIs and services for model management, feature stores, and inference pipelines. 

• Implement monitoring, logging, and observability tools to ensure performance, availability, and reliability of AI services. 

Model Serving & MLOps Enablement 

• Operationalize ML model serving at scale using frameworks such as TensorFlow Serving, TorchServe, KServe, Seldon Core, or custom inference services. 

• Create reusable MLOps components for data preprocessing, training orchestration, model validation, and deployment. 

• Develop automation to reduce ML model deployment time, enforce versioning, and enable rollback/upgrade capabilities. 

• Work closely with data scientists to translate research workflows into production-grade, scalable services. 

Collaboration & Best Practices 

• Partner with AI researchers, data engineers, and platform engineers to deliver integrated solutions. 

• Champion engineering excellence by promoting design documentation, code reviews, CI/CD best practices, and testing automation. 

• Contribute to a culture of shared ownership, transparency, and internal open-source development.