Senior AI Engineer

Minimum requirements (Advantageous):

• Advanced commercial Node.js experience.
• Advanced Type Script.
• Strong backend software engineering and API design.
• Strong PostgreSQL knowledge.
• Solid understanding of relational data modelling and database performance.
• Practical understanding of LLMs and modern AI application development.
• Understanding of embeddings, semantic similarity and vector search.
• Experience with or strong understanding of vector databases / pgvector.
• Understanding of graph databases and knowledge graphs.
• Understanding of agentic AI, tool calling and multi-step AI workflows.
• Experience building integrations between different systems and APIs.
• Comfortable with asynchronous processing, queues, scheduled jobs and event-driven architectures.
• Strong Git and modern software-development practices.
• Experience using AI coding agents/coding assistants as part of serious software development, not simply autocomplete.
• AWS, Docker, ECS, S3, Redis, Bedrock, OpenAI, Anthropic, Mastra, MCP, RAG architectures, pgvector, Neo4j or other graph databases.
• OAuth/OIDC, event-driven architectures, workflow engines and infrastructure as code.

• Build production AI applications and agentic systems using Node.js and Type Script.
• Design backend services, APIs, integrations and event-driven systems.
• Build agentic workflows where AI agents use tools, data and other agents to complete real business tasks.
• Work with LLMs from multiple providers and understand model selection, context management and tool/function calling.
• Design and implement AI memory and organisational knowledge systems.
• Work extensively with PostgreSQL, including schema design, complex queries, performance and production data architecture.
• Implement embeddings, semantic search, RAG and vector retrieval, including PostgreSQL/pgvector and dedicated vector databases where appropriate.
• Understand and work with graph data and graph databases, including relationships between people, systems, processes, documents and organisational knowledge.
• Build integrations with internal and external systems through APIs, MCP servers and other tool interfaces.
• Design reliable long-running AI processes involving scheduled events, human approvals and asynchronous execution.
• Use modern AI coding agents as a core part of the development process while maintaining engineering quality, testing and code review.
• Help establish engineering patterns, reusable components and technical standards for AI-first development.