Lead Artificial Intelligence Engineer

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.

Preferred Qualifications & Experience:

• Bachelor’s 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.
• Master's 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.

Experience Level

Senior Level