Closed listing
Principal Agentic AI Architect
- Mississauga, ON, Canada
Full-time
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Full-time
- Closing date
- Experience
- 12+ years of software engineering experience, including 5+ years building enterprise AI/ML platforms.
- Posting language
- English
- Posted
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Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Position: Principal Agentic AI Architect
Location: Mississauga, ON (Hybrid)
Employment Type: Full-Time
Experience Required: 12 + Years
Position Overview:
We are seeking an experienced Principal AI Platform Architect to lead the design and delivery of enterprise-scale Agentic AI and AI Platform solutions. The ideal candidate will have deep expertise in AI/ML architectures, multi-agent systems, RAG frameworks, cloud-native platforms, and enterprise software engineering.
Key Responsibilities:
- Define and drive the enterprise AI platform and Agentic AI architecture strategy.
- Design scalable AI frameworks enabling multiple business use cases across the organization.
- Lead development of multi-agent ecosystems, RAG solutions, and intelligent automation platforms.
- Establish AI governance, observability, security, and operational best practices.
- Collaborate with business and technology leaders to identify and implement AI-driven innovations.
- Mentor engineering teams and provide architectural leadership on AI initiatives.
Required Skills:
- Bachelor's or master's degree in computer science, Artificial Intelligence, Engineering, or a related field.
- 12+ years of software engineering experience, including 5+ years building enterprise AI/ML platforms.
- Proven experience designing and delivering Agentic AI solutions, AI platforms, multi-agent systems, and autonomous workflows.
- Strong knowledge of agent orchestration, planning, reasoning, memory management, tool calling, retrieval systems, and AI governance.
- Hands-on experience with frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, Google ADK, or similar.
- Expertise in RAG architectures, vector databases, embeddings, enterprise knowledge management, and document intelligence solutions.
- Experience building scalable AI orchestration platforms, reusable components, observability frameworks, and governance controls.
- Strong background in cloud-native architectures, distributed systems, microservices, APIs, and platform engineering.
- Solid understanding of AI monitoring, evaluation frameworks, security, compliance, and Responsible AI principles.
- Excellent stakeholder management, communication, and leadership skills.
- Experience working within Agile, Waterfall, and Hybrid delivery models.
More about this job
Responsibilities
Define enterprise AI platform and Agentic AI architecture strategy, and lead the design and delivery of scalable AI frameworks, multi-agent ecosystems, RAG solutions, and intelligent automation platforms. Establish governance, observability, security, and operational practices while collaborating with business and technology leaders and mentoring engineering teams.
Requirements
Requires 12+ years of software engineering experience, including 5+ years building enterprise AI/ML platforms, with proven experience delivering Agentic AI solutions, multi-agent systems, and autonomous workflows. Requires a bachelor's or master's degree in a related field, expertise in AI orchestration and RAG, cloud-native and distributed systems, AI monitoring and governance, and strong leadership and stakeholder skills.
Skills
- Agentic AI Architecture
- AI/ML Platforms
- Multi-Agent Systems
- Agent Orchestration
- RAG Architectures
- LangGraph
- LangChain
- Semantic Kernel
- CrewAI
- AutoGen
- Google ADK
- Vector Databases
- Cloud-Native Architecture
- Distributed Systems
- AI Governance
- Responsible AI
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Management & Leadership
- Data & Analytics
- Consulting
Keywords
- Agentic AI
- AI Platform Architecture
- Artificial Intelligence
- Machine Learning
- Multi-Agent Systems
- Autonomous Workflows
- Agent Orchestration
- Planning
- Reasoning
- Memory Management
- Tool Calling
- Retrieval-Augmented Generation
- LangGraph
- LangChain
- Semantic Kernel
- CrewAI
- AutoGen
- Google ADK
- Vector Databases
- Embeddings
- Document Intelligence
- Cloud-Native Architecture
- Distributed Systems
- Microservices
- APIs
- Platform Engineering
- AI Governance
- Observability
- Responsible AI