Staff Software Engineer - Agentic AI Systems
Summary
Lead architecture and deployment of production-grade agentic AI systems that orchestrate multi-step reasoning, memory, and tool use for enterprise workflows.
Job Title
Staff Software Engineer - Agentic AI Systems
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.