AI Solutions Architect

About Escalon

Escalon is a Toronto-based AI consultancy built by engineers. We help enterprises redesign their workflows around AI, then engineer, test and run those systems in production.

Our roots are in high-performance software, quality engineering and program delivery across gaming, SaaS and enterprise systems. That discipline is what makes AI hold up in the real world.

About the role

Our clients are large organizations deciding where AI creates measurable advantage and how to build it safely. They need the agents, applications and data foundations behind that work to run in production, not just in a demo.

As an AI Solutions Architect, you are the technical lead on that work. You take a business problem that is still loosely defined, shape it into an architecture a client can trust, and stay close to the build so what ships matches what was designed. Your designs have to hold up in real enterprise environments: existing systems, strict security reviews, messy data and teams that need to adopt the result.

You work directly with client leaders and engineers, and alongside our strategy, engineering and quality teams. You will be in the room for early discovery conversations, you will make the architecture decisions, and you will explain those decisions clearly to people who care about different things: cost, risk, speed and reliability.

This is a remote-friendly role for someone who is comfortable owning ambiguous problems end to end, and who cares as much about how a system behaves on its worst day as on its best.

What you'll do

• Translate business goals and constraints into clear technical architectures for AI-enabled workflows, agents and LLM applications.
• Lead technical discovery with clients: map their systems, data and processes, and identify where AI can safely take on work.
• Design cloud architectures that integrate large language models with enterprise systems, knowledge sources and APIs.
• Choose models, retrieval approaches and orchestration patterns, and document the trade-offs in cost, latency, accuracy and risk.
• Build security, access control and data privacy into every design, and take designs through client security and architecture reviews.
• Define how AI systems are evaluated, monitored and governed, so quality can be measured before and after release.
• Guide engineers through implementation, review their work against the architecture, and unblock hard technical problems.
• Prepare architecture documents, diagrams and proposals, and present recommendations to technical and executive audiences.
• Contribute to Escalon's reusable patterns, reference architectures and delivery practices as the team grows.

What you need

• Substantial experience designing and delivering cloud-based systems at enterprise scale.
• Hands-on experience integrating large language models into real applications, including retrieval, tool use and agent patterns.
• A solid grounding in security, identity and access control, and data privacy, and experience working through enterprise security reviews.
• Strong systems design skills: integration patterns, APIs, data flows, reliability and failure handling.
• Experience turning ambiguous business requirements into concrete technical plans that engineering teams can build.
• Clear written and verbal communication, with the ability to explain technical trade-offs to non-technical stakeholders.
• Comfort working directly with clients and with distributed teams.

Nice to have

• Experience in consulting, professional services or another client-facing delivery environment.
• Experience evaluating and testing non-deterministic systems, such as LLM evaluation suites or red-teaming.
• Familiarity with more than one major cloud platform and its AI services.
• Experience in a regulated industry, such as financial services, healthcare or the public sector.
• Experience with data pipelines, knowledge graphs or enterprise search.