Sr. Software Engineer

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
• Hands-on experience with multi-agent orchestration and supervisor patterns.
• Familiarity with LangChain, LangGraph, LangSmith, or similar frameworks.
• Knowledge of RAG pipelines, memory management, and evaluation of agent performance.
• Evaluation and Optimization
• Familiarity with agent evaluation frameworks (e.g., LangSmith evals, benchmark datasets, unit tests for agent workflows).
• Experience designing custom evaluation metrics for reasoning steps, tool use correctness, and RAG pipeline performance.
• Ability to balance automatic evaluation (synthetic benchmarks, self-play) with human-in-the-loop evaluation for complex workflows.
• Cloud & Infrastructure
• Strong software engineering foundation with experience in cloud environments (AWS preferred).
• Knowledge of containerized deployment and backend integration.
• Programming & Development
• Proficiency in Python; experience with backend systems and API integration.
• 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.