We are seeking a proactive and technically skilled Agentic & AI Tech Ops Engineer to join our AI CoE. This role is crucial for ensuring the reliability, scalability, and efficient operation of our cutting-edge AI and Agentic AI systems in production environments.

You will be responsible for deploying, monitoring, maintaining, and troubleshooting our AI agents and the underlying infrastructure. Working closely with AI developers, architects, and data scientists, you will implement and manage AgenticOps/MLOps practices, automate operational tasks, and contribute to building a robust and resilient operational framework for our AI initiatives.

Key Responsibilities :

Deployment & Infrastructure Management :

- Deploy, configure, and manage AI models, agentic systems, and supporting infrastructure in cloud (e.g., GCP) and on-premise environments.

- Implement and maintain CI/CD pipelines for AI/ML models and agentic applications (MLOps/AgentOps).

- Manage and optimize cloud resources, ensuring cost-effectiveness and scalability for AI workloads.

- Collaborate with infrastructure teams to ensure network, storage, and compute resources meet the demands of AI systems.

Monitoring, Logging & Alerting :

- Develop and implement comprehensive monitoring, logging, and alerting solutions for AI agents and infrastructure to ensure high availability and performance.

- Proactively identify and address potential issues, performance bottlenecks, and anomalies in production AI systems.

- Track key operational metrics and create dashboards for system health and performance.

Incident Response & Troubleshooting :

- Provide operational support for production AI systems, including incident response, root cause analysis, and resolution of technical issues.

- Develop and maintain runbooks and standard operating procedures for common operational tasks and incident management.

- Participate in on-call rotations as needed to support critical AI services.

Automation & Operational Excellence :

- Automate routine operational tasks, deployment processes, and system maintenance activities using scripting (e.g., Python, Bash) and automation tools.

- Contribute to the development and enforcement of operational best practices, security standards, and compliance requirements for AI systems.

- Work with development teams to improve the deployability, manageability, and observability of AI applications.

Collaboration & Documentation :

- Collaborate effectively with AI developers, data scientists, AI architects, and other stakeholders to ensure smooth transitions from development to production.

- Maintain clear and comprehensive documentation for system configurations, operational procedures, and troubleshooting guides.

- Provide feedback to development teams on operational aspects and system performance.

Required Qualifications & Experience :

- Bachelors degree in Computer Science, Information Technology, Engineering, or a related technical field.

- 4-7+ years of experience in a MLOps or AgentOps role, preferably supporting AI/ML or data-intensive applications.

- Hands-on experience with cloud computing platforms (e.g., Google Cloud Platform - especially Vertex AI) and managing cloud-based infrastructure.

- Proficiency in scripting languages such as Python, Bash, or PowerShell for automation.

- Experience with CI/CD tools and practices (e.g., Bitbucket, GitLab CI, GitHub Actions).

- Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration.

- Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack, Datadog, Google

Cloud Monitoring, Langfuse).

- Understanding of networking concepts, security best practices, and infrastructure-as-code (IaC) principles (e.g., Terraform, Ansible).

- Strong troubleshooting and problem-solving skills with an analytical mindset.

- Excellent communication skills and ability to work collaboratively in a team environment.

- A proactive approach to identifying and resolving issues and improving system reliability.

Preferred Qualifications & Experience :

- Master's degree in a relevant field.

- Specific experience in MLOps or AgentOps, including deploying and managing machine learning models or large language model applications in production.

- Familiarity with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

- Understanding of agentic AI concepts and the operational challenges they present.

- Experience with managing vector databases or other specialized data stores for AI.

- Knowledge of data pipeline tools (e.g., Apache Airflow, Kubeflow Pipelines).

- Relevant cloud certifications (e.g., Google Cloud Professional ML Engineer).

- Experience working in an agile development environment.