Context Engineer

🕒 August 7

🇨🇦 Canada – Remote

💵 $120k - $140k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

👷🏻‍♀️ Engineer

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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CapIntel

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2019

💸 Finance

💳 Fintech

🤝 B2B

💰 $11M Series A on 2022-06

Finance • Fintech • B2B

CapIntel is a company that provides investment comparison and proposal tools designed to help clients understand and engage with their investment products. Their platform is used by some of the biggest banks in the world to support financial advisors in presenting easy-to-understand proposals that highlight experience, value, and financial education. CapIntel's tools allow for interactive and visual comparisons, helping clients to grasp the impact of their investment choices on future financial goals. With a focus on elevating the client experience, CapIntel aims to make financial advice accessible and comprehensible, building stronger client-advisor relationships and business success.

📋 Description

• Design and implement LLM-powered features into the core application via model APIs such as Anthropic, OpenAI, and Cohere • Architect and maintain retrieval-augmented generation pipelines connecting language models to knowledge bases, databases, and live data sources • Manage context window strategy to optimise accuracy, cost, and latency • Design and implement agentic workflows for multi-step autonomous tasks • Build guardrail and output validation layers for reliable and compliant AI behaviour • Develop reusable agent primitives, prompt templates, and workflow components • Build evaluation frameworks for context effectiveness, output quality, and agent reliability • Monitor deployed AI systems for failure patterns and implement mitigation strategies • Collaborate with Product, Product Engineering, Implementation, and Data teams to translate requirements and proofs of concept into production AI specifications • Upskill the engineering team on context engineering and agentic best practices

🎯 Requirements

• 5+ years of professional software engineering experience • At least 1–2 years working with LLMs in a production context • Strong experience with Python or Node and API-integrated backend services • Hands-on experience with an orchestration or execution framework • Working knowledge of RAG architecture, vector databases such as Pinecone, pgVector, or AWS OpenSearch, and semantic search • Familiarity with context management techniques including summarisation, chunking, session splitting, and memory strategies • Experience building or consuming REST APIs and integrating third-party services • Experience collaborating with cross-functional teams in a fast-paced, high-growth environment • Strong problem-solving instincts and willingness to learn and adapt • Nice-to-have: experience with MCP or similar tool-integration standards • Nice-to-have: familiarity with LLMOps practices, tracing, observability, and model versioning • Nice-to-have: exposure to multi-agent architectures and orchestration patterns • Nice-to-have: knowledge of AI output validation, context safety, and governance in regulated financial services • Nice-to-have: familiarity with AWS, Docker, or Kubernetes

🏖️ Benefits

• Variable pay may be included depending on the role • Equity may be included depending on the role • Comprehensive benefits • Flexible time off • Dedicated opportunities for growth and development • Perks and benefits designed to support growth and well-being