Lead Research Engineer II, Smart Virtual Systems

About the Role

The Advanced Remanufacturing and Technology Centre (ARTC) is looking for a Lead / Senior Solution Architect to drive the design and implementation of enterprise-scale agentic AI systems for industrial and enterprise applications. This role focuses on architecting AI‑enabled platforms and workflows that leverage LLMs, tool orchestration, autonomous agents, enterprise integrations, and scalable cloud‑native systems to support intelligent operations and decision‑making. You will work closely with engineering teams, AI researchers, and business stakeholders to shape technical strategy, define system architectures, and lead the delivery of production‑grade AI solutions. We are looking for experienced architects who combine strong software engineering and system design capabilities with deep interest in emerging agentic AI technologies and practical enterprise adoption.

Key Responsibilities

Architect and lead development of enterprise‑scale agentic AI systems and platforms

Design AI orchestration workflows involving LLMs, tools, agents, and enterprise integrations

Define scalable, secure, and reliable system architectures and engineering practices

Collaborate with stakeholders to translate operational needs into AI‑enabled solutions

Evaluate and adopt emerging technologies related to agentic AI and enterprise AI systems

Provide technical leadership and mentorship to engineering teams

Job Requirements

Degree in Computer Science, Software Engineering, or related fields

Strong experience in software architecture, backend systems, or enterprise platform development

Experience designing scalable cloud‑native or distributed systems

Familiarity with LLMs, AI application architectures, and orchestration frameworks

Interest or experience in agentic AI systems, automation workflows, or multi‑agent applications

Strong communication, problem‑solving, and stakeholder management skills

Good to have

Experience with cloud platforms such as Azure, AWS, or GCP

Familiarity with Docker, Kubernetes, CI/CD pipelines, or MLOps practices

Experience building AI‑enabled enterprise applications, copilots, or intelligent automation solutions

Exposure to AI governance, observability, or security considerations for AI systems

Experience working with manufacturing, industrial systems, or enterprise digital transformation initiatives

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