This role offers an exciting opportunity to work in AI/ML Development and Operations (DevOps) engineering, working within a dynamic team that values reliability and continuous improvement.

The successful candidate will contribute to the deployment and maintenance of AI/ML systems in production, gaining hands-on experience with MLOps best practices and infrastructure automation.

This position provides a structured environment for developing core competencies in ML system operations, DevOps practices, and production ML monitoring, with guidance from seasoned professionals.

- Assist in the deployment and maintenance of machine learning models in production environments under direct supervision, learning containerization technologies like Docker and Kubernetes.

- Support CI/CD pipeline development for ML workflows, including model versioning, automated testing, and deployment processes using tools like Azure DevOps.

- Monitor ML model performance, data drift, and system health in production environments, implementing basic alerting and logging solutions.

- Contribute to infrastructure automation and configuration management for ML systems, learning Infrastructure as Code (IaC) practices with tools like Terraform or CloudFormation.

- Collaborate with ML engineers and data scientists to operationalize models, ensuring scalability, reliability, and adherence to established MLOps procedures and best practices.

Required :

What Skills & Experience You Should Bring :

- 5 to 8 Years of professional experience in in DevOps, MLOps, or systems engineering environment.

- Bachelor's degree in Computer Science, Engineering, Information Technology, or a closely related technical field.

- Trimble's Professional ladder typically requires four or more years of formal education.

- Expertise in working with Microsoft Azure and its services including ML/AI (Azure ML, Azure DevOps, etc.)

Must Have :

- Highly proficient in Python or other scripting languages (Shell / Bash / PowerShell / Perl) for automation scripting and system integration (Must have).

- Strong experience of containerization technologies (Docker) and orchestration concepts (Kubernetes).

- Strong experience of DevOps principles and practices, with understanding of CI/CD concepts and system administration.

- Strong experience with CI/CD tools such as Jenkins, GitHub Actions, or ArgoCD.

- Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, New Relic).

- Hands-on with version control systems (Git) and collaborative development workflows.

- Experience with data engineering concepts and technologies (SQL, NoSQL, ETL pipelines).

- Experience with MLOps tools and frameworks (Kubeflow, MLflow, Weights & Biases, or similar).

Preferred :

- Experience with other cloud platforms (GCP, AWS) is a plus.

- Hands-on experience in machine learning concepts and the ML model lifecycle from development to production.

- Experience of AIOps and incident management platforms like Moogsoft, BigPanda, PagerDuty, or Opsgenie.

- Working knowledge with model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime).

- Working knowledge of security best practices for ML systems and data governance.

About Our Division : Construction Management Solutions (CMS)

Trimble's Construction Management Solutions (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.