In this role, you will bridge the gap between brand-new AI research and practical engineering. You will lead the charge in integrating autonomous AI agents into the daily workflows of our architects, hardware designers, and researchers. You will be responsible for understanding internal engineering bottlenecks, researching the latest academic and industry advancements, and translating them into deployed production tools using our AI stack.
What you'll be doing:
Team Leadership & Strategy: Lead and mentor a mixed team of SW engineers and researchers, balancing the agility of research with the responsibility of production software engineering.
Product Engineering: Partner with internal engineering teams (Hardware, Architecture, Research) to analyze their workflows, identify friction points, and architect Agentic AI solutions to accelerate their work.
Applied Research: Actively monitor the latest AI trends, academic papers, and technical blogs (LLMs, Agents, RAG). Translate these insights into concrete features and tools for the team.
Agent Architecture: Be responsible for the design of scalable frameworks for AI agent interaction, ensuring they can reason, plan, and orchestrate tasks within complex engineering environments.
SW-Focused MLOps: Define and implement the software lifecycle for AI models – from fine-tuning to deployment – utilizing our AI deployment tools and ensuring seamless CI/CD.
Production Deployment: Ensure reliable operation of agent fleets, optimizing for latency and resource consumption while maintaining security and access controls.
What we need to see:
MSc or Ph.D. or equivalent experience in Computer Science, Computer Engineering, or a related field.
8+ overall years of hands-on experience in software engineering, with a proven track record of leading or mentoring technical teams.
3+ years of managerial experience.
Tech Stack: Expert-level proficiency in Python, with strong familiarity with modern AI frameworks (PyTorch, LangChain, LlamaIndex).
Research to Production: A proven ability to digest complex technical literature (papers, blogs) and implement those concepts in a working software product.
Product Management Approach: Experience in requirements gathering, user research, or internal tool development.
Operational Excellence: Proven understanding of modern CI/CD, containerization (Docker/Kubernetes), and MLOps standards on the software stack side.
Ways to stand out from the crowd:
Domain Hybrid: Deep background in both AI/ML and High-Performance Computing (HPC) systems.
Agentic Experience: Hands-on experience building autonomous agents that interact with external tools or APIs.
Internal Tools: Prior experience building developer platforms or productivity tools for engineering organizations.
