Senior AI Architect - Direct to Market

Wawanesa Insurance•Calgary, AB

25d•CA$130,000 - CA$165,000•Hybrid

About The Position

Responsible for designing and evolving the agentic AI platform capabilities that power Direct 2 Market (D2M) interactions across voice, messaging, and digital channels. D2M is a strategic venture to build Wawanesa's digitally native distribution model, reimagining how members (customers) discover, quote, buy, and manage insurance through AI-first experiences. This role brings agentic experiences to life by combining hands-on technical depth with architectural leadership across LLM-driven workflows, orchestration, memory, tool use, model access, and human-in-the-loop operations. It sits at the intersection of AI, experience, and platform, ensuring agentic capabilities are not just prototyped, but integrated into cohesive, scalable, secure, production-ready enterprise solutions that support end-to-end member journeys. This role is deeply embedded in an AI-first delivery model, acting as a technical leader for how agentic capabilities are shaped, implemented, evaluated, governed, and expanded over time.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related discipline; graduate degree in AI/ML is an asset.
10+ years in enterprise solution, platform, or software architecture, including architecting or delivering production LLM, GenAI, or agentic AI applications.
Demonstrates strong ownership of outcomes, direct communication, and willingness to challenge ideas constructively.
Comfortable operating in a high-trust, fast-moving environment with emphasis on experimentation, accountability, and continuous learning.
Deep experience designing and delivering LLM-powered applications, including conversational systems and agentic workflows, using modern frameworks such as LangGraph, LangChain, or Copilot Studio.
Practical expertise in orchestration patterns, prompt and context engineering, RAG, vector databases, embeddings, evaluation approaches, and deployment at scale.
Working knowledge of Model Context Protocol (MCP), tool/function calling, and modern agent-to-system interoperability patterns.
Proven software engineering capability with experience building and deploying enterprise production systems.
Experience integrating AI capabilities with enterprise systems, APIs, events, and operational platforms in complex environments.
Strong cloud architecture and engineering knowledge (AWS preferred), including services that support real-time voice, messaging, orchestration, and scalable execution.
Experience defining reference architectures, reusable platform capabilities, technical standards, and implementation patterns for enterprise AI solutions.
Strong technical communication and facilitation skills, with the ability to explain architecture, trade-offs, and implementation patterns clearly to both technical and non-technical stakeholders.

Nice To Haves

Experience working in AI-augmented or AI-first delivery environments is strongly preferred.
Insurance, financial services, or other regulated-industry experience is an asset.

Responsibilities

Own the architecture and direction for agentic platform capabilities across voice, messaging, and digital channels, ensuring consistent underlying intelligence across interaction modes.
Design multi-step agentic workflows that coordinate tools, APIs, and decisioning across the end-to-end member journey.
Define how conversational state, memory, and context persist across interactions so members can move between AI and human-assisted journeys without losing continuity.
Architect the cloud platform capabilities required for real-time agentic experiences, including speech, messaging, orchestration, knowledge retrieval, model access, and tool execution.
Architect integrations with core enterprise systems and supporting platforms, including policy, billing, CRM/contact center, document generation, and third-party data services.
Develop patterns for authorization, execution controls, auditability, explainability, observability, and progressive delegation of AI actions.
Define the evaluation and quality framework, including golden datasets, regression tests, hallucination and safety evaluations, latency, and cost benchmarks for agents and RAG pipelines.
Guide the development of pilots, prototypes, and reference implementations to validate new agentic capabilities through rapid test-and-learn cycles.
Lead technical working sessions, design reviews, code reviews, and architecture discussions to align teams on patterns, trade-offs, and implementation decisions.
Influence the broader enterprise agentic platform direction by contributing learnings, patterns, and reusable capabilities from D2M into the organization's shared agentic AI foundation.
Partner with Data, Integration, Platform, Cybersecurity, and Governance Architects to align D2M agentic AI solutions with enterprise architectures, standards, and Responsible AI requirements.
Contribute to Architecture Review Board, Technical Review Board, and Architecture Working Group forums, and author Architecture Decision Requests for significant D2M AI decisions.