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.