About the RoleAs 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.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.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.What You’ll DoDesign and implement LLM-powered features into our core application via model APIs (e.G. Anthropic, OpenAI, Cohere), with a focus on reliability and production-readinessArchitect and maintain retrieval‑augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sourcesManage 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 latencyDesign and implement agentic workflows enabling the platform to handle multi‑step,



autonomous tasksBuild guardrail and output validation layers that constrain model behaviour and ensure AI features act within well‑defined, compliant boundariesDevelop reusable agent primitives, prompt templates, and workflow components that other engineers can build on independentlyBuild evaluation frameworks to measure context effectiveness, output quality, and agent reliability in productionMonitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cyclesCollaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specificationsAct as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practicesWhat We’re Looking For5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production contextStrong experience with Python or Node and building API‑integrated backend servicesHands‑on experience with an orchestration or execution frameworkWorking knowledge of RAG architecture, vector databases (e.G. Pinecone, pgVector, AWS OpenSearch), and semantic searchFamiliarity with context management techniques: summarisation, chunking, session splitting,



and memory strategiesExperience building or consuming REST APIs and integrating with third‑party servicesComfortable collaborating with cross‑functional teams in a rapid‑paced, high‑growth environmentStrong problem‑solving instincts and a willingness to learn and adapt as the field evolvesNice to HaveExperience with the Model Context Protocol (MCP) or similar tool‑integration standardsFamiliarity with LLMOps practices: tracing, observability (e.G. LangSmith, Datadog), and model versioningExposure to multi‑agent architectures and orchestration patternsKnowledge of AI output validation, context safety, and governance considerations particularly relevant in regulated industries like financial servicesFamiliarity with AWS or cloud‑based infrastructure and containerised deployments (Docker, Kubernetes)Ability to communicate technical concepts clearly to both technical and non‑technical partnersCompensationAt 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 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. For roles based in or eligible to work from Ontario, the expected base salary range is: $120,000 - $140,000 CAD.#J-18808-Ljbffr

📌 Context Engineer - C$120,000 - C$140,000 A Year (Winnipeg)
🏢 CapIntel
📍 Winnipeg