CapIntel - Context Engineer
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Requirements
• 5+ years of professional software engineering experience, with at least 1–2 years working with LLMs in a production context • Strong experience with Python or Node and building API-integrated backend services • Hands-on experience with an orchestration or execution framework • Working knowledge of RAG architecture, vector databases (e.g. Pinecone, pgVector, AWS OpenSearch), and semantic search • Familiarity with context management techniques: summarisation, chunking, session splitting, and memory strategies • 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 • Strong problem-solving instincts and a willingness to learn and adapt as the field evolves • Experience with the Model Context Protocol (MCP) or similar tool-integration standards • Familiarity with LLMOps practices: tracing, observability (e.g. LangSmith, Datadog), and model versioning • Exposure to multi-agent architectures and orchestration patterns • Knowledge of AI output validation, context safety, and governance considerations particularly relevant in regulated industries like financial services • Familiarity with AWS or cloud-based infrastructure and containerised deployments (Docker, Kubernetes) • Ability to communicate technical concepts clearly to both technical and non-technical partners • 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—$140,000 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.
Responsibilities
• 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 • Architect and maintain retrieval-augmented generation (RAG) pipelines, connecting language models to internal knowledge bases, databases, and live data sources • 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 • 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 in production • Monitor deployed AI systems for failure patterns and implement mitigation strategies, feeding learnings back into continuous improvement cycles • Collaborate with Product, Product Engineering, Implementation, and Data teams to translate business requirements, and proof of concepts into production AI system specifications • Act as an internal practitioner and resource helping upskill the broader engineering team on context engineering principles and agentic best practices
Benefits
• 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.
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