Lead Research Engineer II, Smart Virtual Systems
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
- 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
- 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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