Context Engineer
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Job Description
Context Engineer at CapIntel responsible for integrating LLMs into the core platform with a focus on reliability. Collaborating with various teams to enhance advisor and client experience.
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
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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