Staff Software Engineer - Agentic AI Systems

**Job Title** Staff Software Engineer - Agentic AI Systems **Job Description** **About the Role** - We are seeking a Staff Agentic AI Engineer to lead the architecture, implementation, and production deployment of advanced agentic AI systems. - In this role, you will serve as a technical authority for multi-agent systems across Cognichip, driving long-horizon autonomous workflows that integrate proprietary models, semiconductor design tools, and cloud infrastructure. - You will design systems that reason across multiple steps, manage memory and knowledge grounding, and operate reliably in production over extended periods. - This is a senior individual contributor leadership role. - You will define architectural patterns, raise engineering standards, mentor other engineers, and partner closely with Applied AI, Product Engineering, and Platform teams to translate cutting-edge research into scalable enterprise solutions. - Success in this role is measured not by prototypes, but by robust, production-grade agentic systems shipped to customers. **Key Responsibilities** **Technical Leadership & Architecture** - Own the end-to-end architecture of agentic AI workflows, including reasoning pipelines, memory systems, RAG, evaluation frameworks, and orchestration patterns. - Define best practices for supervisor/sub-agent coordination, fault tolerance, long-horizon reasoning, and system robustness. - Serve as Cognichip’s internal expert on agentic AI system design and production deployment. **Build & Operate Agentic Systems** - Design and implement multi-step autonomous agents with advanced memory, Retrieval-Augmented Generation (RAG), and integrations to tools, APIs, and enterprise data sources. - Deliver production-grade workflows deployed on cloud platforms (AWS preferred), with strong observability, monitoring, and reliability guarantees. - Drive continuous improvement of agent quality, cost efficiency, and performance in real customer environments. **Evaluation & Optimization** - Define and implement comprehensive evaluation pipelines for agentic systems: - Task success / failure classification - Grounding accuracy - Reasoning robustness - Tool-use reliability - Long-horizon completion rates - Establish regression testing and benchmarking strategies using frameworks such as LangSmith or custom evaluation infrastructure. - Balance automated evaluation with human-in-the-loop feedback for complex workflows. **Cross-Functional Collaboration** - Partner with Applied AI researchers to productionize new capabilities. - Work with backend/platform engineers to integrate agents with cloud infrastructure and enterprise systems. - Collaborate with product managers to translate semiconductor workflows into agent-driven user experiences. **Organizational Impact** - Set technical direction for agentic AI systems across teams. - Mentor senior and mid-level engineers. - Raise engineering standards around agent architecture, evaluation, and production readiness. **Required Qualifications** - Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field. - 8–12+ years of professional software engineering experience. - 3+ years building and deploying production-grade agentic AI systems. - Deep hands-on experience with: - Multi-agent orchestration frameworks (LangGraph, LangChain, LangSmith, or equivalents) - RAG pipelines and memory systems - Agent evaluation methodologies - Strong proficiency in Python and backend cloud services (AWS preferred). - Proven track record delivering complex AI systems into production. **Preferred Qualifications** - Contributions to open-source AI projects or frameworks. - Experience with multi-agent orchestration patterns at scale. - Knowledge of reinforcement learning, planning algorithms, or autonomous reasoning. - Track record of deploying agentic AI systems in production at scale. **What We Offer** - The chance to work on state-of-the-art AI systems that push the boundaries of autonomy and reasoning. - A collaborative environment where engineering meets research. - Competitive compensation and equity in a fast-growing AI startup. - A culture that values ownership, curiosity, and technical excellence.