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Trimble Inc. Logo Trimble Inc. · IT - Software Development

Site Reliability Engineer

India, Chennai Employment contract · Job Posted June 09, 2026
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Job Description

Shape the Future of Intelligent Operations as a Site Reliability Engineer (AI Ops / ML Ops). Are you passionate about deploying, monitoring, and scaling machine learning systems in production environments? Trimble's Construction Management Solutions (CMS) division is looking for a driven, early-career Site Reliability Engineer to join our high-performing team in Chennai. In this role, you will help build and manage robust AI infrastructure, bridging the gap between cutting-edge data science and resilient cloud operations.

Job Responsibility

  • Assist in the deployment and maintenance of machine learning models in production under direct supervision, building skills in containerization and orchestration architectures
  • Support the development of robust continuous integration and deployment pipelines for ML workflows, including model versioning, automated testing, and release processes
  • Monitor production ML model performance, detect data drift, and track system health by implementing foundational logging, alerting, and metrics solutions
  • Contribute to infrastructure automation and configuration management for machine learning workloads, learning to treat infrastructure as software
  • Partner closely with ML engineers and data scientists to operationalize complex models, ensuring reliability, scale, and strict adherence to established operational patterns

Requirements

  • 1 to 2 years of professional experience in a DevOps, MLOps, or systems engineering environment
  • Bachelor's degree in Computer Science, Engineering, Information Technology, or a closely related technical field
  • Direct experience with Microsoft Azure cloud platforms and its specialized ecosystem services (such as Azure ML and Azure DevOps)
  • Proficiency with Python or other scripting languages (Shell / Bash / PowerShell) for rapid system integration and task automation
  • Foundational understanding of containerization (Docker), basic orchestration concepts (Kubernetes fundamentals), and version control system workflows (Git)
  • Solid baseline knowledge of fundamental DevOps principles (CI/CD, system administration) and a basic understanding of the end-to-end machine learning model lifecycle

Nice to have

  • Familiarity with MLOps tracking tools and open-source frameworks (MLflow, Kubeflow, DVC, or similar)
  • Basic experience with monitoring software suites (Prometheus, Grafana, ELK stack)
  • Exposure to Infrastructure as Code (IaC) configuration tools like Terraform or Ansible
  • Knowledge of database systems, data pipeline technologies, or model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime)
  • Experience with cross-platform (Windows/Linux) command-line administration and a basic understanding of cloud security best practices for AI workloads

What we offer

  • Structured environment to accelerate technical skills
  • Direct guidance from experienced engineering professionals
  • Projects that improve productivity, quality, safety, transparency and sustainability
  • Collaborative and supportive team
  • Entrepreneurial spirit empowering proactive doers
  • Flexible work arrangements
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