Context Engineer

CapIntel; Montreal (administrative region); On-site; CAD 120,000 - 150,000; Full time

Job summary

CapIntel is seeking a Context Engineer to join our production-focused engineering team. You’ll integrate large language models into CapIntel’s Node/TypeScript platform, owning features from design to production support.

This senior role emphasizes reliability and scalable AI workflows. You’ll collaborate with product, data, and engineering teams to translate business needs into production AI systems, define context management patterns, and help shape CapIntel’s AI discipline across the

Qualifications

• 5+ years of professional software engineering experience building and operating production systems.
• Proficient in Node/TypeScript; Python is a welcome addition.
• 1–2+ years building LLM-backed features in production.
• Knowledge of RAG architecture, vector databases, and semantic search.
• Experience debugging non-deterministic systems.
• Experience introducing a new technical practice to a team.
• Experience building or consuming REST APIs.
• Ability to communicate complex concepts clearly across teams.

Responsibilities

• Build and ship LLM-powered features in Node/TypeScript apps.
• Maintain retrieval-augmented generation pipelines with internal data sources.
• Define context management strategies for optimization.
• Design agentic workflows for multi-step autonomous tasks.
• Implement guardrails and output validation for AI features.
• Develop reusable agent primitives and prompt templates.
• Create evaluation frameworks to measure context effectiveness.
• Monitor AI systems and drive continuous improvement.
• Help define engineering standards for context engineering at CapIntel.
• Collaborate with product and data teams to productionize proofs of concepts.
• Upskill the broader team on context engineering practices.

Skills

Node/TypeScript

Production systems

LLM integration

REST APIs

Cross-functional collaboration

Tools

Docker

Kubernetes

LangSmith

Langfuse

Datadog

Job description

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 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 CAD - $150,000 CAD

Notsureyou meet every requirement?

We care most about mindset:yourdrive,curiosityandcommitmentto deliveringgreat work.While experiencematters, weknow thatcareersaren’talways linear. If this role excitesyouand you believe you can make an impactwith us,we want to hear from you.

Whyyou'llenjoy working here

Learn more aboutlife atCapIntelon our Careers page, including the virtues that inspire how we work and theperksand benefits designed to support your growth and well-being.We’rea team built on trust, respect, and collaboration. This powerseverything we doand creates a space to challenge and elevate each otheras we work towards our shared vision.If this speaks to you,we’dbe excited to have you with us.