Context Engineer

CapIntel is a software platform built for wealth management enterprises to help financial advisors explain complex investment strategies to their clients. Advisors at some of the biggest banks across North America are winning trust by using CapIntel to easily compare investments and create compelling, educational presentations. Ultimately, we're focused on investors getting better service, understanding their investments, and feeling at ease knowing their future is secure.

Since launching in 2019,CapIntelhas seen rapid adoption and industry recognition, earning top placements in Deloitte’s Technology Fast 50 Canada and Fast 500 North America in 2025, ranking us among the fastest -growing technology companies. To support this momentum,we’regrowing our teamrapidly—investingin people who drive innovation at scale to expand our impact across the North American wealth management industry.

About the Role

CapIntel is a software platform built for wealth management enterprises to help financial advisors explain complex investment strategies to their clients. Advisors at some of the biggest banks across North America are winning trust by using CapIntel to easily compare investments and create compelling, educational presentations. Ultimately, we're focused on investors getting better service, understanding their investments, and feeling at ease knowing their future is secure.

Since launching in 2019,CapIntelhas seen rapid adoption and industry recognition, earning top placements in Deloitte’s Technology Fast 50 Canada and Fast 500 North America in 2025, ranking us among the fastest -growing technology companies. To support this momentum,we’regrowing our teamrapidly—investingin people who drive innovation at scale to expand our impact across the North American wealth management industry.

About the Role

As a Context Engineer at CapIntel, you'll sit at the intersection of software engineering and applied AI. This is a senior engineering role first, with a specialisation in integrating large language models into production systems. It is hands‑on and production‑focused rather than research‑oriented: you'll be writing code in our core application roughly 75% of the time, and you'll own the features you build through to production support.

Our platform is built in Node and TypeScript, and you'll be working in it every day. You'll help define for how language models are integrated into that platform, and for how our engineering team adopts agentic workflows.

You'll be embedded in a development team working closely with engineers, product managers, and domain experts. As the first practitioner in this discipline at CapIntel, you'll also help define what context engineering looks like here, setting the patterns and practices the broader team can build on.

This role is ideal for a strong backend engineer who has already shipped customer‑facing AI features, cares about production reliability over demo‑day performance, and is energised by working in a discipline that's still taking shape.

What You’ll Do

• Build and ship LLM‑powered features in our Node/TypeScript application via model APIs (e.g. Anthropic, OpenAI, Bedrock), owning them from design through production support
• Help architect and maintain retrieval‑augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources, with accountability for retrieval quality as well as retrieval plumbing
• Collaborating with Architecture, manage context window strategy, determining what information enters the model, when, in what format, and at what level of compression to optimise for accuracy, cost, and latency
• Design and implement agentic workflows enabling the platform to handle multi‑step, autonomous tasks, and diagnose them when they behave unexpectedly, including wrong tool selection, silent failures, and partial state
• Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well‑defined, compliant boundaries
• Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently
• Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability against real production traffic
• Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles
• Help define the engineering standards for this discipline at CapIntel, covering patterns, review criteria, testing approach, and documentation
• Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements and proofs of concept into production AI systems
• Act as an internal practitioner and resource, helping upskill the b