Solutions Architect in Toronto

Bits In Glass (BIG) is a high-growth AI and automation consulting firm with offices across Canada, the United States, India, and the United Kingdom. Our portfolio spans more than 10 leading technologies across AI, cloud, data, automation, and business applications. Recognized globally as a Great Place to Work and the recipient of multiple partner excellence awards, BIG is built on collaboration, innovation, and delivering measurable business outcomes for enterprise clients.

BIG is excited to be partnering with Convene AI, an AI-focused technology company developing innovative solutions that leverage artificial intelligence to solve real-world business challenges. As part of this partnership, we are looking for talented professionals to join BIG and work directly on Convene AI projects and initiatives. You will initially be hired as an employee of Bits In Glass, while working closely with the Convene AI team and contributing to the development and growth of their AI solutions. The role is expected to remain on BIG’s payroll for approximately 12–18 months, with the intention of transitioning to a permanent position directly with Convene AI following this period, subject to business requirements and transition arrangements.

This is the highest-leverage technical role at Convene AI. Every architectural decision you make is inherited by multiple client engagements, and the accelerators and standards you build determine how fast the next pod moves. The mandate has three parts:

• Shaping and Pre-Sales: Join client conversations during the sales cycle to assess technical feasibility, and scope what can realistically reach production in 8 to 12 weeks. Own the architecture decisions that determine engagement profitability: model selection, build versus buy, reuse of existing accelerators versus bespoke work, and hosting approach.
• Architecture Governance Across Pods: Run architecture review at kickoff and at defined gates for every live engagement. Make the calls that carry risk beyond one pod: data residency, private versus hosted inference, identity and access design, network isolation, and integration patterns with systems of record.
• Platform, Standards and Reuse: Own the Convene AI reference architecture and accelerator library, and decide what gets harvested from an engagement versus rebuilt properly for reuse. Set the standards every pod inherits for agent design, evaluation, observability, security, and cost management.
• Technical Leadership: Mentor GenAI Engineers and Data Engineers across pods through design and code review, and raise the technical ceiling of the delivery organisation.

WHAT WE'RE LOOKING FOR:

• 10 or more years building and shipping enterprise software, including at least 2 years on production LLM or agentic systems.
• A track record as the technical owner across multiple concurrent client engagements.
• Deep hands-on capability: Python, cloud architecture (AWS, Azure, or GCP), distributed systems, and API and integration design.
• Production experience with agent architectures, retrieval at enterprise scale, evaluation, and the operational side of AI systems.
• Enterprise architecture fluency: identity and access, security posture, data residency, network isolation, and model hosting options.
• Commercial literacy and the judgement to recommend against a deal that cannot be delivered.
• Legally entitled to work in Canada and willing to travel to client sites.

BONUS POINTS:

• Architecture leadership at a consultancy or systems integrator.
• Depth in a regulated industry with named systems.
• Experience building a reference architecture or accelerator library that other teams genuinely reused.
• Published writing, conference talks, or open-source work on applied AI architecture.

WHY JOIN CONVENE AI:

• Your decisions compound. Architecture set here is inherited by every engagement that follows.
• You define the standard rather than inherit someone else's.
• Genuine breadth: pre-sales shaping through production operations, across multiple industries.
• Direct working relationship with the founders and with senior AI operators.
• Meaningful equity at the stage where technical decisions determine whether the delivery model scales or stalls.