As an AI Engineer Team Lead, you will:

Lead, mentor and professionally develop a team of AI engineers.
Define the team’s technical roadmap, priorities, architecture and engineering standards while remaining actively involved in architecture, research, design and development.
Shape AI adoption across the company by building reusable AI capabilities, connecting organizational data and systems with models and agents and enabling R&D and product teams to develop AI-powered workflows and products.
Manage model evaluation and benchmarking across quality, accuracy, latency, throughput, infrastructure requirements and cost.
Be responsible for turning emerging AI technologies into practical, scalable and production-ready solutions.
Identify and prioritize high-value AI use cases with development, product, research, and business teams.
Lead AI initiatives from research and POC through architecture, development, deployment and production.
Ensure strong engineering practices including testing, observability, Docker, CI/CD, GitOps, Kubernetes and Helm.
Work closely with DevOps, infrastructure, research, development and product teams to deliver end-to-end AI solutions.

If you have

At least 2 years of management experience.
Proven experience in designing reusable AI capabilities including RAG, GraphRAG, knowledge systems, agents, MCP/A2A integrations, tools and APIs.
Experience in guiding model-serving and inference strategies using technologies such as vLLM, LiteLLM, SGLang, TGI or Triton.
Ability to design systems that are reliable, scalable, secure, observable and cost-efficient.
Strong hands-on software engineering and system design skills.

It would be great if you also have:

A background in cybersecurity or security-data products.
Experience building a new team or introducing AI practices across an engineering organization.