Lead AI Engineer

Its fun to work in a company where people truly BELIEVE in what theyre doing!

Job Description:

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/Agent Ops).
• 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.

Preferred Qualifications & Experience:

• Bachelors degree in Computer Science Information Technology Engineering or a related technical field.
• 4-7 years of experience in a MLOps or Agent Ops 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.
• Masters degree in a relevant field.
• Specific experience in MLOps or Agent Ops 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.

Why Join Us

• Play a critical role in operationalizing cutting-edge Agentic AI and AI systems for a global industry leader.

• Gain hands-on experience with the latest MLOps Agent Ops and cloud technologies.
• Work in a dynamic innovative and collaborative AI Center of Excellence.
• Opportunity to significantly impact the reliability and efficiency of transformative AI solutions.
• Competitive salary bonus and benefits package.