AI DevOps Engineer II
Trimble Inc. (company site, opens in a new tab)3–5 yrsChennaiOn-siteDirect career pageEst. 12–20 LPA
Posted 15h ago
Reach people at Trimble Inc.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Trimble Inc. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Develop, automate, and support Trimble's cloud and AI/ML projects including maintaining CI/CD and MLOps pipelines.
- Implement and enhance AI-driven observability, monitoring, incident management, and self-healing solutions to improve system uptime and model availability.
- Design, configure, and maintain AWS infrastructure including disaster recovery, backups, and AI/ML model deployments using IaC tools like Terraform and automation scripting.
Minimum Requirements
- 3 to 5 years of relevant experience in DevOps/MLOps or cloud engineering roles within large-scale enterprise environments.
- Proficiency with AWS services, IaC tools (Terraform or CloudFormation), container technologies (Docker, Kubernetes, ECS/EKS), and automation scripting (Python, Shell, PowerShell).
- Experience with monitoring and AIOps tools such as DataDog, Grafana, Nagios, or New Relic.
- AWS certifications such as Certified Solutions Architect or Certified DevOps Engineer preferred.
Ideal Candidate Profile
- Experienced in operating and scaling AI/ML infrastructure on AWS with strong automation and IaC expertise, particularly Terraform.
- Skilled in integrating AI-driven monitoring and proactive incident management for high-availability cloud environments.
- Familiar with advanced AI competencies including LLM deployment, RAG, AI-assisted developer tools, and model-serving frameworks within enterprise cloud contexts.