Site Reliability Engineer

About the role

Dedicated to the world's tomorrow, Trimble is a technology company delivering solutions that enable our customers to work in new ways to measure, build, grow and move goods for a better quality of life. Core technologies in positioning, modeling, connectivity and data analytics connect the digital and physical worlds to improve productivity, quality, safety, transpare ncy and sustainability. From purpose-built products and enterprise lifecycle solutions to industry cloud services, Trimble is transforming critical industries such as construction, geospatial, agriculture and transportation to power an interconnected world of work. For more information about Trimble (NA SDAQ: TRMB), v isit: www.trimble.com About Our Division: Construction Management Solutions (CMS) Trimble's CMS division is dedicated to transforming the construction industry. We provide technology solutions that streamline and optimize workflows for preconstruction, project management, and field operations. By connecting the physical and digital worlds, we help our customers improve productivity, efficiency, and project outcomes. What Makes This Role Great: This role offers an exceptional entry point into the highly sought-after field of AI Ops and ML Ops. Working within a dynamic team that values reliability and continuous improvement, you will gain hands-on experience with production-level infrastructure automation and MLOps best practices. With direct guidance from experienced engineering professionals, you will have a structured environment to accelerate your technical skills, operationalize advanced ML models, and work on systems powering global enterprise platforms. Key Exciting Responsibilities Production ML Deployment: Assist in the deployment and maintenance of machine learning models in production under direct supervision, building skills in containerization and orchestration architectures. CI/CD & Pipeline Management: Support the development of robust continuous integration and deployment pipelines for ML workflows, including model versioning, automated testing, and release processes. Model & Infrastructure Observability: Monitor production ML model performance, detect data drift, and track system health by implementing foundational logging, alerting, and metrics solutions. Infrastructure as Code (IaC): Contribute to infrastructure automation and configuration management for machine learning workloads, learning to treat infrastructure as software. Cross-Functional Collaboration: Partner closely with ML engineers and data scientists to operationalize complex models, ensuring reliability, scale, and strict adherence to established operational patterns. Essential Skills & Experience Professional Experience: 1 to 2 years of professional experience in a DevOps, MLOps, or systems engineering environment. Education: Bachelor's degree in Computer Science, Engineering, Information Technology, or a closely related technical field. Cloud Core Ecosystem (Must-Have): Direct experience with Microsoft Azure cloud platforms and its specialized ecosystem services (such as Azure ML and Azure DevOps). Automation Scripting: Proficiency with Python or other scripting languages (Shell / Bash / PowerShell) for rapid system integration and task automation. Containerization & Tools: Foundational understanding of containerization (Docker), basic orchestration concepts (Kubernetes fundamentals), and version control system workflows (Git). Core Conceptual Literacy: Solid baseline knowledge of fundamental DevOps principles (CI/CD, system administration) and a basic understanding of the end-to-end machine learning model lifecycle. Bonus Points For: MLOps Frameworks: Familiarity with MLOps tracking tools and open-source frameworks (MLflow, Kubeflow, DVC, or similar). Observability Tooling: Basic experience with monitoring software suites (Prometheus, Grafana, ELK stack). Infrastructure Automation: Exposure to Infrastructure as Code (IaC) configuration tools like Terraform or Ansible. Data & Runtime Frameworks: Knowledge of database systems, data pipeline technologies, or model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime). Operating Systems & Governance: Experience with cross-platform (Windows/Linux) command-line administration and a basic understanding of cloud security best practices for AI workloads. Logistics: Location: Chennai, India Work Mode: Onsite Job Type: Full-Time Why You'll Lo