MANAGER I DEVOPS

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MANAGER I DEVOPS

5/13/26

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

Own and drive the enterprise MLOps and data platform architecture, enabling scalable ML workloads and data pipelines across business units Lead development of enterprise-wide MLOps and Generative AI frameworks, standardizing ML lifecycle, deployment, and governance practices Drive DataOps and feature engineering initiatives, managing large-scale data pipelines from ingestion to model serving Design and implement end-to-end ML systems including training, validation, deployment, monitoring, and retraining workflows Lead DevOps practices and CI/CD implementation for ML and data platforms, ensuring reliable and automated deployments Own Infrastructure as Code (IaC) strategy using Terraform, Terragrunt, and CloudFormation across multi-account cloud environments Establish and enforce security, governance, and compliance frameworks including IAM, RBAC, encryption, and auditability Build and maintain observability frameworks for monitoring, logging, model performance, and system reliability Enable and scale self-service ML platforms, improving developer productivity and reducing deployment timelines Drive cloud cost optimization (FinOps) and operational efficiency across ML and data workloads Lead cross-functional collaboration with data science, engineering, and business teams to deliver production-ready AI solutions Evaluate and integrate emerging technologies, including Generative AI (LLMs, RAG, multi-model systems), into enterprise platforms Job Requirements

Bachelor’s degree in Computer Science, Engineering, or related field 6+ years of experience in MLOps, DevOps, Platform Engineering, or Data Engineering Strong experience building and operating enterprise-scale ML platforms Hands-on experience with AWS (preferred) or other cloud platforms Strong experience with CI/CD tools (Jenkins, GitHub Actions, etc.) Experience with Infrastructure as Code (Terraform, Terragrunt, CloudFormation) Experience with containerization and orchestration (Docker, Kubernetes) Strong understanding of ML lifecycle management and MLOps tools (MLflow, SageMaker, Databricks, etc.) Experience with data engineering systems (ETL/ELT pipelines, feature stores, large-scale data processing) Experience implementing observability and monitoring frameworks Strong understanding of security and governance in cloud and ML systems

Preferred