Solutions Architect (Agentic AI)
- Toronto, ON, Canada
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Original job description
Iris's client, one of the world's largest multinational Investment banking and financial services corporations, is looking to hire a Senior AI Solutions Architect (Agentic AI) opportunity.
Work location: Toronto, ON (Hybrid Onsite)
Job description:
Principal AI Solution Architect – Agentic AI & Enterprise AI Platforms
We are seeking a Principal AI Solution Architect with 15+ years of experience in enterprise architecture, cloud platforms, and AI engineering, with strong expertise in Generative AI, Agentic AI, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG).
Key Responsibilities:
- Design and implement enterprise-grade GenAI platforms, agentic workflows, multi-agent systems, and RAG solutions.
- Define reusable AI architecture patterns, MCP integrations, enterprise tool connectivity, and reference frameworks.
- Drive AI platform strategy, governance, security, Responsible AI, observability, and production readiness.
- Architect cloud-based AI solutions using Microsoft Azure AI Foundry, Azure OpenAI, AWS Bedrock, or equivalent platforms.
- Collaborate with business stakeholders, clients, and engineering teams to translate requirements into scalable AI solutions.
- Lead technical workshops, architecture reviews, proof of concepts, and enterprise AI adoption initiatives.
- Mentor engineering teams and establish standards for CI/CD, GenAIOps, monitoring, and secure deployment.
Required Skills:
- Strong experience in Generative AI, Agentic AI, LLMs, RAG, prompt engineering, and multi-agent orchestration.
- Hands-on knowledge of MCP, Python, AI frameworks such as Lang Graph, AutoGen, or Lang Chain.
- Experience with cloud AI services, vector databases, APIs, enterprise integrations, and data platforms.
- Understanding of AI security, identity and access management, governance, guardrails, and observability.
- Excellent solution architecture, client engagement, stakeholder management, and technical leadership skills.
- Preferred Experience: Enterprise banking or financial services, AI Centers of Excellence (CoE), reusable AI accelerators, and large-scale production AI implementations.
More about this job
Responsibilities
Design enterprise-grade generative and agentic AI platforms, workflows, multi-agent systems, and RAG solutions, while defining reusable architecture patterns and integrations. Lead AI platform strategy, governance, security, production readiness, stakeholder engagement, technical workshops, and engineering standards.
Requirements
Requires 15+ years of experience in enterprise architecture, cloud platforms, and AI engineering, with strong expertise in generative AI, agentic AI, LLMs, RAG, and multi-agent orchestration. Candidates should have hands-on knowledge of MCP, Python, AI frameworks, cloud AI services, enterprise integrations, and AI security, along with strong architecture and technical leadership skills.
Skills
- Generative AI
- Agentic AI
- Large Language Models
- Retrieval-Augmented Generation
- Prompt Engineering
- Multi-Agent Orchestration
- Model Context Protocol
- Python
- LangGraph
- AutoGen
- LangChain
- Cloud AI Services
- Vector Databases
- AI Governance
- AI Security
- Solution Architecture
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Management & Leadership
- Finance & Accounting
- Consulting
Keywords
- Generative AI
- Agentic AI
- Enterprise AI Platforms
- Enterprise Architecture
- Cloud Platforms
- AI Engineering
- Large Language Models
- Retrieval-Augmented Generation
- Prompt Engineering
- Multi-Agent Systems
- Multi-Agent Orchestration
- Model Context Protocol
- MCP Integrations
- Python
- LangGraph
- AutoGen
- LangChain
- Microsoft Azure AI Foundry
- Azure OpenAI
- AWS Bedrock
- Vector Databases
- APIs
- Enterprise Integrations
- Data Platforms
- AI Governance
- Responsible AI
- AI Security
- Identity and Access Management
- GenAIOps
- CI/CD