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Context Engineer

EngineerEngineerFull TimeRemoteSeniorTeam 51-200Since 2019H1B No SponsorCompany SiteLinkedIn

Location

Canada

Posted

39 days ago

Salary

$120K - $140K / year

Seniority

Senior

Bachelor Degree5 yrs expEnglishAWSNode.jsPython

Job Description

Context Engineer

CapIntel

• 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

Job 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.

Benefits

  • Variable pay
  • Equity
  • Comprehensive benefits
  • Flexible time off
  • Dedicated opportunities for growth and development

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