Applied AI Engineer: Requirements Management & LLM Systems

Renesas Electronics Corp.; United States; Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Private Health Insurance

Meal Allowance

Remote Working Abroad Program

Professional Development Grant

Learning Resources

Mental Health Benefit

Time Off – 22 vacation days

Office Comforts

Job summary

Altium seeks a skilled professional to advance LLM-powered requirements and hardware design features in a dynamic, collaborative team. You will implement agentic loops, tool-calling and orchestration while collaborating with engineers, PMs and designers in an agile setup to deliver robust AI-driven capabilities.

You will also build evaluation metrics, optimize pipelines, and drive end-to-end lifecycle activities from experimentation to deployment and ongoing enhancement.

Qualifications

• Profound knowledge of LLM engineering and production-ready environments.
• Advanced Python development skills to implement such systems.
• Experience with agentic loops, tool-calling, MCP servers, and benchmarking pipelines.

Responsibilities

• Develop agentic flows using off-the-shelf LLMs and modern harnessing methods for hardware design and requirements.
• Collaborate with software engineers, product managers, and designers in an agile, sprint-based environment to ship features.
• Build evaluation pipelines and design metrics to measure AI-driven system performance.
• Extend test suites and run benchmarks on AI systems for speed, cost, and accuracy.
• Participate in the full AI product lifecycle from experimentation to deployment and improvement.

Skills

LLM engineering

Python development

Agent orchestration

Experiment planning

Autonomous task execution

Multi-disciplinary teamwork

Tools

MCP servers

Tool-calling

Agentic loops

Evaluation pipelines

Job description

Altium seeks a skilled professional to advance LLM-powered requirements and hardware design features in a dynamic, collaborative team. You will implement agentic loops, tool-calling and orchestration while collaborating with engineers, PMs and designers in an agile setup to deliver robust AI-driven capabilities.

You will also build evaluation metrics, optimize pipelines, and drive end-to-end lifecycle activities from experimentation to deployment and ongoing enhancement.