Senior Databricks Engineer building production data pipelines for V4C’s enterprise consultancy clients. Owning data quality, PySpark transformations, monitoring, debugging, and Lakehouse performance.
About the role
- Context Engineer integrating reliable LLM and agentic workflows into CapIntel’s wealth management software platform. Building RAG pipelines, guardrails, evaluations, and production AI capabilities for financial advisors.
Responsibilities
- 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
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