Full Stack Developer – Agentic AI

About the role

As a member of our Machine Learning group, you will design and build agentic Generative AI systems that help chip design engineers accelerate their daily workflows. The role requires strong backend expertise, a product mindset, and curiosity to embed LLM‑powered agents into Linux‑based toolchains used in semiconductor design.

Key responsibilities

• Architect, build, and operate the agentic infrastructure, including LLM orchestration, tool‑calling layers, multi‑agent workflows, and hosting services.
• Design and implement software solutions that translate chip design engineers' workflows into reliable, automated agent‑driven systems.
• Integrate agents and services with Linux environments, in‑house tools, and third‑party EDA tools.
• Manage production agentic and ML workflows to ensure performance, high availability, and resource efficiency.
• Deploy and maintain production services with proactive monitoring to catch regressions before they affect users.
• Build automated evaluation frameworks for retrieval quality, agent execution, and LLM output accuracy across releases.
• Partner with CAD teams to integrate pipelines and agents into CI/CD, ensuring reliable deployments and safe rollbacks.
• Implement access controls, authentication, and secret‑handling practices for sensitive design data.
• Collaborate closely with ML engineers, researchers, and customer teams.

Required profile

• 5+ years of full‑stack development experience with relational and NoSQL databases (e.g., MongoDB, SQL).
• Strong object‑oriented programming and system design skills; proficient in TypeScript and Python for both frontend and backend.
• Hands‑on experience building dynamic web applications using React and Node.js or Python.
• Familiarity with microservices, Docker, Kubernetes, and CI/CD pipelines (GitLab, GitHub Actions, Jenkins).
• Proven experience designing, developing, and scaling real‑time distributed systems.
• Experience deploying LLM‑powered applications, agents, or related components (vector databases, RAG pipelines) in production.
• Excellent communication and collaboration abilities to bridge engineering teams and explain technical trade‑offs.

Required skills

• TypeScript
• Python
• React
• Node.js
• MongoDB
• SQL databases
• Docker
• Kubernetes
• CI/CD (GitLab, GitHub Actions, Jenkins)
• LLM orchestration / RAG pipelines
• Microservices architecture
• Linux environments