At Slalom, we co-create modern technology and software products with clients who are ready to accelerate their digital product development. We imagine how things can be made better, then set out to realize what’s possible—driving innovation with quality, resilience, and purpose. By blending design, product engineering, analytics, and automation, we build the custom-built software and data products of tomorrow.

Job Title

Agentic AI Architect or Sr. Architect - Intelligence Engineering (For Vacant Role)

Mission

As a Senior Architect or Architect in Intelligence Engineering, you’ll design and deliver innovative AI/ML and agentic AI solutions as part of intelligent products and automating / re-envisioning human workflows on Amazon Web Services, Azure, and Google Cloud. This includes architecting multi-agent systems, LLM-powered autonomous workflows, retrieval-augmented generation (RAG) pipelines, and enterprise-grade AI governance frameworks using cutting‑edge orchestration frameworks, tool‑use protocols, and cloud-native AI services. You’ll help clients move from isolated proofs of concept to secure, scalable, observable systems that create real business value, while helping craft projects in their initial phases and delivering them with a team.

Who you’ll work with

You’ll join a tightly knit product team —engineers, product leaders, designers, and data practitioners—who co‑create modern software with our customers. We move from discovery to delivery together, pairing on code, pressure‑testing ideas with real users, and turning learnings into ship‑ready increments. You’ll collaborate with customer stakeholders and field teams to bring outside‑in insights into the roadmap,



and you’ll help enable clients on the practices and tools we use so they can sustain value long after launch. Think craft‑driven Builders who care about outcomes, not just output.

What you’ll do

- Provide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosity
- Design end‑to‑end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patterns
- Architect multi‑agent orchestration systems using frameworks such as Strands Agents SDK, OpenAI Agents SDK, Google ADK, LangGraph or similar for autonomous reasoning, decision‑making, and task execution
- Design and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross‑system interoperability with enterprise systems
- Build advanced retrieval‑augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re‑ranking, and multi‑source data synthesis
- Design and deliver AI and ML solutions across AWS, Azure, and GCP, using the right mix combination of cloud‑native data services, ML tooling, LLM platforms, and software engineering practices
- Build in Python and, where helpful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows,



and production services
- Recommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, and MLOps/LLMOps at scale
- Implement evaluation frameworks (e.g., RAGAS, DeepEval, LangSmith) to measure task success rates, tool‑call accuracy, and reasoning integrity for GenAI systems
- Build guardrails for safety, compliance, and performance monitoring including human‑in‑the‑loop (HITL) approval workflows, escalation policies, and sandbox isolation
- Define AI governance frameworks including model risk management, responsible AI practices, regulatory compliance, and authorization boundaries for autonomous decision‑making
- Explain model and system behavior to both technical and non‑technical audiences, including leading deep technical presentations, workshops, and architecture conversations
- Collaborate with Product Owners to apply Slalom’s agile process and lead the initiation, delivery, and transition of projects in a client‑facing role
- Lead and mentor engineers and machine learning practitioners. Lead smaller projects (3 to 5 people) as the technical lead from project initiation to delivery
- Build trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practice
- Will be delivery‑focused approximately 85–95% of the time
- Willingness to travel up to 50%, at peak times

Hybrid/In office: We are hybrid but there are expectations that if your team leader requires you onsite that you are able to meet those expectations/requirements

Slalom is committed to fair and equitable compensation practices. For t

📌 Agentic AI Architect (Toronto)
🏢 Slalom
📍 Toronto