- Professional
- Escritório em Chennai
Your Title: AI Operations Engineer
Job Location: Chennai
Our Department: CMS
Are you passionate about deploying, monitoring, and scaling machine learning systems in production environments and eager to contribute to robust AI infrastructure within a collaborative team?
What You Will Do This role offers an exciting opportunity to begin a career in AI/ML operations 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 direct guidance from experienced 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 Jenkins, GitLab CI, or GitHub Actions.
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.
What Skills & Experience You Should Bring
Required:
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.
Foundational knowledge of DevOps principles and practices, with understanding of CI/CD concepts and basic system administration.
Proficiency in at least one relevant programming language (e.g., Python, Bash) with focus on automation scripting and system integration.
Understanding of containerization technologies (Docker) and basic orchestration concepts (Kubernetes fundamentals).
Familiarity with version control systems (Git) and collaborative development workflows.
Basic understanding of machine learning concepts and the ML model lifecycle from development to production.
Preferred:
Experience with cloud computing platforms (AWS, Azure, GCP) and their ML/AI services (SageMaker, Azure ML, Vertex AI).
Familiarity with MLOps tools and frameworks (MLflow, Kubeflow, DVC, or similar).
Basic experience with monitoring and observability tools (Prometheus, Grafana, ELK stack).
Understanding of Infrastructure as Code (IaC) tools like Terraform, Ansible, or CloudFormation.
Experience with Linux system administration and command-line tools.
Knowledge of database systems and data pipeline technologies.
Exposure to model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime).
Basic understanding of security best practices for ML systems and data governance.
