Jobs at Trimble

Build and maintain automated cloud, product, and AI/ML deployments; manage AWS infrastructure through Terraform and other IaC tools; maintain CI/CD and MLOps pipelines; implement monitoring, AIOps, observability, self-healing, disaster recovery, and incident-management solutions; automate operations with Python and scripting; support Kubernetes, GPU/EKS clusters, model-serving frameworks, vector stores, and LLM workflows while meeting service availability and performance targets.

Design, build, and maintain Workday reports and dashboards (Advanced, Matrix, Composite); create complex calculated fields; optimize Workday Prism and specialized hubs; troubleshoot data accuracy and performance; enforce security/privacy controls; translate HRIS/HCM data into actionable people analytics for stakeholders.

Lead full-lifecycle development of scalable, cloud-native SaaS transportation solutions. Architect front-end and back-end components, establish quality standards for AI-generated code, promote AI-first development practices, mentor international engineering teams, and improve system performance, security, and maintainability. The role requires extensive full-stack engineering expertise across React, .NET, Azure, Docker, CI/CD, and SQL Server, with a focus on transportation and logistics technology.

Own the enterprise data experience and Common Data Environment product domain for Trimble Connect. Define strategy and roadmap, guide AI-assisted metadata, classification, and import mapping capabilities, and partner with engineering leaders on data models, APIs, authorization, and architecture. Engage strategic customers, influence cross-functional teams, and represent Trimble as a construction technology thought leader.

Supports enterprise Azure reliability and security operations across Windows virtual machines and SQL Servers. Responsibilities include patch orchestration, vulnerability scanning and CVE remediation, Azure governance, RBAC, WAF and Zero Trust controls, automation with PowerShell, Python or Terraform, incident triage, and root-cause analysis. Collaborates with global engineering teams while developing expertise in cloud security, observability, DevOps pipelines, and AI-assisted operational automation.

Leads AI-driven SecOps strategy and cloud security architecture across a large Azure environment. Governs Windows and SQL Server security, patch orchestration, vulnerability remediation, Zero Trust, Azure policies, WAF controls, disaster recovery, and audit compliance. Establishes automation standards using PowerShell, Python, Terraform, and AI tools while mentoring global engineering teams and presenting security posture and risk metrics to executive stakeholders.

Coordinate Professional Services projects by managing project setup, consulting requests, resource requests, system-of-record maintenance, customer inquiries, project metrics, deliverables, and internal communication repositories. Collaborate with managers across teams, maintain data integrity, identify operational bottlenecks, improve workflows, and resolve issues while balancing competing priorities and supporting US-based teams on a night-shift schedule.

Design, develop, and maintain scalable RESTful APIs, integrate with backend systems, and collaborate with teams to deliver solutions. Enhance API performance and documentation, while ensuring security and compliance.

The Staff Software Engineer will lead complex problem-solving initiatives, drive AI adoption, ensure technical integrity of products, and improve cloud infrastructures.

As a Lead Software Engineer, you will solve complex technical challenges, lead by example, and assist in scalable system design and development, focusing on advanced .NET technologies and observability tools.

As a Staff Software Engineer, you will lead the resolution of complex problems and maintain the technical integrity of the product suite while influencing long-term technology strategy and managing technical collaborations across various teams.

Lead a small India-based corporate development team to evaluate M&A, partnerships, and venture investments. Build complex financial models (3-statement, DCF, comps, LBO), prepare investment memos and executive presentations, manage CRM pipeline and deal workflows, conduct market and thematic research, and mentor analysts while driving process improvements and automation.