Lead AI Engineer

It's fun to work in a company where people truly BELIEVE in what they're 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.