About the Role

We are seeking an AI Engineer to design, implement, and deploy advanced agentic AI systems. In this role, you’ll build production-ready AI agents that can reason across multiple steps, leverage a mixture of proprietary models, integrate with semiconductor design tools, and operate autonomously over a long period of time.

You’ll work with state of the art frameworks to create pipelines that combine LLM-based reasoning, knowledge grounding, and multi-agent orchestration. This is a high-impact role where you’ll partner with research and engineering teams to translate cutting-edge chip design workflows into reliable, scalable agentic solutions for real-world use cases.

Key Responsibilities

● Build Agentic Systems – Implement multi-step reasoning agents with advanced memory, Retrieval-Augmented Generation (RAG), and integrations to tools, databases, and APIs.

● Evaluate and Optimize Agent Performance – Define and implement evaluation pipelines for agentic systems, including success/failure classification, grounding accuracy, reasoning robustness, tool-use reliability, and long-horizon task completion. Use metrics and benchmarks to continuously improve performance in production environments.

● Orchestrate & Optimize – Design supervisor/sub-agent patterns, enable coordination across agents, and apply best practices for robustness, performance, and scalability.

● Deploy & Evolve – Deliver production-grade agentic AI workflows on cloud platforms (AWS preferred), monitor and evaluate agent performance, and continuously fine-tune for quality and efficiency.

● Collaborate & Translate – Work with product managers, researchers, and engineers to transform complex chip design workflows into agent-driven, end-to-end solutions.

Desired Skillset

● Agentic AI Systems

1. Hands-on experience with multi-agent orchestration and supervisor patterns.
2. Familiarity with LangChain, LangGraph, LangSmith, or similar frameworks.
3. Knowledge of RAG pipelines, memory management, and evaluation of agent performance.

● Evaluation and Optimization

1. Familiarity with agent evaluation frameworks (e.g., LangSmith evals, benchmark datasets, unit tests for agent workflows).
2. Experience designing custom evaluation metrics for reasoning steps, tool use correctness, and RAG pipeline performance.
3. Ability to balance automatic evaluation (synthetic benchmarks, self-play) with human-in-the-loop evaluation for complex workflows.

● Cloud & Infrastructure

1. Strong software engineering foundation with experience in cloud environments (AWS preferred).
2. Knowledge of containerized deployment and backend integration.

● Programming & Development

1. Proficiency in Python; experience with backend systems and API integration.
2. Familiarity with modern software engineering practices (GitHub, CI/CD pipelines, testing frameworks).

Required Qualifications

● Bachelor’s or Master’s degree in Computer Science, Software Engineering or a related field.

● 5-10 years of experience in software development in cloud environment

● 2+ years of experience with hands-on experience building and deploying production-grade agentic AI systems with real-world applications.

● Solid working knowledge of Retrieval-Augmented Generation (RAG), agent performance evaluation, and hands-on experience with LangGraph and LangSmith platforms.

● Proficiency in Python and familiarity with backend cloud services (preferably AWS).

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.