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.