Head of Artificial Intelligence

Head of AI TMRW Silicon The mission AI is not a side project for us. It is how we intend to multiply what every team can achieve. We are looking for a Head of AI who will turn that ambition into reality: someone who can build, run and constantly improve self-hosted AI systems that people actually rely on every day, and who measures success by the impact on the organization, not by the elegance of the stack. You will own AI end to end, from GPUs to agents to the people who use them. You will decide where models run, which models we use, how they are turned into useful tools, and how those tools keep getting better as the company grows. What you will own AI that works for people - Turn AI into a true force multiplier for every team, identifying where it can deliver the highest leverage and making it happen. - Design, build and maintain agent harnesses around self-hosted models (tool use, context management, memory, orchestration, permissions), turning raw models into reliable, productive agents. - Work side by side with teams to understand their workflows, remove friction and drive real adoption. - Continuously measure and improve quality, usability and impact, using evaluations, telemetry and user feedback to guide every iteration. Models and platform - Select, evaluate, deploy and fine-tune open-weight LLMs and other models, staying ahead of a fast-moving landscape and adopting what delivers real gains. - Build and operate the core platform: inference serving, RAG pipelines, evaluation frameworks, access control and data governance. - Lead the development, training, validation and deployment of custom machine learning models, with solid MLOps practices (experiment tracking, versioning, reproducibility, lifecycle management). Infrastructure - Design and run compute infrastructure across neocloud, cloud and on-premise environments, choosing the right option for each workload. - Own performance, reliability, security and cost, making sound trade-offs as needs scale. - Ensure AI services are highly available, because when teams depend on AI, it cannot go down. Strategy and team - Define the AI strategy and roadmap, aligned with business priorities and revisited as they evolve. - Hire, lead and grow a high-performing team of AI, ML and infrastructure engineers. - Be the organization's go-to reference on AI: set standards, share knowledge and raise the bar for everyone. What success looks like - Teams across the organization use AI tools daily because they make them faster and better, not because they are told to. - AI capabilities improve visibly over time, with each iteration driven by data and feedback. - The platform is reliable, secure and cost-efficient, and scales smoothly with demand. - A strong team is in place, able to keep AI operations running and improving without depending on a single person. What you bring - Proven experience deploying and operating AI/ML systems in production on self-managed and cloud infrastructure. - Deep hands-on experience with open-weight LLMs: serving, quantization, fine-tuning, RAG and agentic workflows. - Experience designing or customizing agent harnesses around self-hosted models. - Strong knowledge of GPU infrastructure, containerization and orchestration (Docker, Kubernetes) and inference stacks (vLLM, TGI, Triton or similar). - Solid machine learning and data science foundations (Python, PyTorch or similar) and experience with MLOps tooling. - A product mindset: you care about usability, adoption and measurable impact, and you iterate relentlessly based on evidence. - Sound judgment on infrastructure, build-vs-buy and cost decisions. - Leadership that scales: comfortable being hands-on today while building the team that will carry the function tomorrow. - Clear communication with both technical and non-technical colleagues. Bonus points - Experience designing and building on-premise AI clusters. - Experience in regulated environments. - Expertise in AI security, privacy and model governance. - A track record of driving AI adoption across an organization. Why TMRW Silicon - Full ownership: your decisions shape how the whole company works. - High impact and high visibility, with a direct line to leadership. - Challenging, multidisciplinary problems with real-world impact. - The chance to build an AI function that sets the pace for everything else we do.