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, CapIntel has 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're growing our team rapidly—investing in 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 broader engineering team on context engineering principles and agentic best practices
What We're Looking For
5+ years of professional software engineering experience building and operating production systems
Deep, current Node/TypeScript experience. This is a core requirement for the role, Python experience is a welcome addition alongside it
1 to 2+ years building LLM-backed features that external users depend on in production, rather than prototypes or internal tooling
Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgvector, AWS OpenSearch), and semantic search, including an understanding of where retrieval quality degrades and why
Hands-on experience with an orchestration or agent execution framework
Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies
Experience debugging non-deterministic systems
Experience introducing a new technical practice or discipline into a team that didn't have one, and getting other engineers to adopt it
Experience building or consuming REST APIs and integrating with third-party services
Comfortable collaborating with cross-functional teams in a fast-paced, high-growth environment, and making decisions with incomplete information as the field evolves
Ability to communicate technical concepts clearly to both technical and non-technical partners
Nice to Have
Experience in a regulated industry (financial services, healthcare, insurance) and awareness of what compliance requires of AI outputs
Experience with the Model Context Protocol (MCP) or similar tool-integration standards
Familiarity with LLMOps practices: tracing, observability (e.g. LangSmith, Langfuse, Datadog), model versioning, and rollback
Exposure to multi-agent architectures and orchestration patterns
Experience using AI-assisted development tools as an established part of your own workflow
Familiarity with AWS or cloud-based infrastructure and containerised deployments (Docker, Kubernetes)
At CapIntel, we design compensation with intention. Each role is assessed against the impact, skills, and experience it requires, and we align our pay to competitive market data so candidates know what to expect from the start.
Your final offer will reflect your experience, skillset, and location. The listed range is a guideline, and the range for this role may be modified.
Compensation at CapIntel goes beyond base pay. Depending on the role, total rewards may include variable pay, equity, comprehensive benefits, flexible time off, and dedicated opportunities for growth and development.
If you'd like to understand more about our approach, we're happy to walk through it during the hiring process.
For roles based in or eligible to work from Ontario, the expected base salary range is:
$120,000—$150,000 CAD
Not sure you meet every requirement?
We care most about mindset: your drive, curiosity and commitment to delivering great work. While experience matters, we know that careers aren't always linear. If this role excites you and you believe you can make an impact with us, we want to hear from you.
Why you'll enjoy working here
Learn more about life at CapIntel on our Careers page, including the virtues that inspire how we work and the perks and benefits designed to support your growth and well-being. We're a team built on trust, respect, and collaboration. This powers everything we do and creates a space to challenge and elevate each other as we work towards our shared vision. If this speaks to you, we'd be excited to have you with us.

Work arrangement
Yes