Context Engineer

About this role

<div class="content-intro"><p><span data-contrast="auto">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.</span></p> <p><span data-contrast="auto">Since launching in 2019,&nbsp;CapIntel&nbsp;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&nbsp;</span><span data-contrast="auto">fastest</span><span data-contrast="auto">-growing technology companies. To support this momentum,&nbsp;we’re&nbsp;growing our team&nbsp;rapidly—investing&nbsp;in people who drive innovation at scale to expand our impact across the North American wealth management industry.</span><span data-ccp-props="{}">&nbsp;</span></p></div><h2 id="About-the-Role" data-local-id="26cd18725a1b" data-renderer-start-pos="88">About the Role</h2> <p data-renderer-start-pos="104" data-local-id="a89955351da5">As a Context Engineer at CapIntel, you'll sit at the intersection of AI infrastructure and engineering. You will be responsible for how large language models are integrated into our core platform and how our engineering team adopts agentic workflows. This is a hands-on, production-focused role, not a research one. You'll build the systems that make our AI features reliable, accurate, and scalable for the wealth management enterprises that depend on us.</p> <p data-renderer-start-pos="562" data-local-id="3b63622360df">You'll be embedded in development teams working closely with engineers, product managers, and domain experts across the organization to design and deliver LLM-powered capabilities that directly enhance the advisor and client experience. As one of the first practitioners in this discipline at CapIntel, you'll also help define what context engineering looks like here: setting patterns and practices the broader team can build on.</p> <p data-renderer-start-pos="994" data-local-id="87e725434086">This role is ideal for someone who thinks in systems, cares about production reliability over demo-day performance, and is energized by working in a discipline that is evolving quickly.</p> <hr> <h2 id="What-You'll-Do" data-local-id="8df028d8a87d" data-renderer-start-pos="1182">What You'll Do</h2> <ul> <li data-renderer-start-pos="1200" data-local-id="e076c7bed376">Design and implement LLM-powered features into our core application via model APIs (e.g. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readiness</li> <li data-renderer-start-pos="1373" data-local-id="85e1c0ab1f4c">Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources</li> <li data-renderer-start-pos="1536" data-local-id="a4903cf4ab89">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</li> <li data-renderer-start-pos="1717" data-local-id="8148386e4975">Design and implement agentic workflows enabling the platform to handle multi-step, autonomous tasks</li> <li data-renderer-start-pos="1820" data-local-id="c09ca18cd817">Build guardrail and output validation layers that constrain model behaviour and ensure AI features act within well-defined, compliant boundaries</li> <li data-renderer-start-pos="1968" data-local-id="3fb670c1d5ac">Develop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independently</li> <li data-renderer-start-pos="2096" data-local-id="e788d512df73">Build evaluation frameworks to measure context effectiveness, output quality, and agent reliability in production</li> <li data-renderer-start-pos="2213" data-local-id="c9ff17cb20e2">Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles</li> <li data-renderer-start-pos="2360" data-local-id="09b1fa2f0583">Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications</li> <li data-renderer-start-pos="2540" data-local-id="a1c01c5eb70a">Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices</li> </ul> <hr> <h2 id="What-We're-Looking-For" data-local-id="cd807c37bdd0" data-renderer-start-pos="2695">What We're Looking For</h2> <ul> <li data-renderer-start-pos="2721" data-local-id="a8170e9c2aaa">5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production context</li> <li data-renderer-start-pos="2848" data-local-id="93c829e54829">Strong experience with Python or Node and building API-integrated backend services</li> <li data-renderer-start-pos="2934" data-local-id="f0db7fa8b33a">Hands-on experience with an orchestration or execution framework</li> <li data-renderer-start-pos="3002" data-local-id="b06f453b8677">Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgVector, AWS OpenSearch), and semantic search</li> <li data-renderer-start-pos="3124" data-local-id="f99b6fee4dc9">Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies</li> <li data-renderer-start-pos="3241" data-local-id="8f2f17f62625">Experience building or consuming REST APIs and integrating with third-party services</li> <li data-renderer-start-pos="3329" data-local-id="c2a679fa18