Applied AI Engineer
Summary
Builds AI-driven systems for session/context management, retrieval, agentic workflows, and evaluation in a SaaS product, integrating speech/images, optimizing for latency/cost, and ensuring security/autonomy. Core tech: GenAI (RAG, agents), Go/Python, MCP, LangChain, Kubernetes, and API gateways.
Build the intelligence behind the experience: the session and memory model, retrieval across our own estate and the documents that run it, the agentic flows that carry out multi-step work, and the evaluation that tells us whether any of it is right.
What you will do
Build the session and context spine: persistence, resumption, hand-over with the reasoning trail intact, and context resolved from a person's identity and entitlements.
Build retrieval and grounding across structured records and documents, at a cost and latency the product can afford.
Build document intelligence over manuals, engineering drawings, certification directives and contracts, extracted and citable, because for many of our customers those carry as much operational truth as the databases.
Build the agentic flow runtime: multi-step work with tools, and the rules that decide when a human is asked rather than told.
Handle speech and images as first-class input alongside text, so the same request works dictated in a plant room or photographed at an asset.
Build the evaluation harness, and treat it as the gate on every claim we make about autonomy.
Build the language interface over our scheduling and optimisation engines, expressing trade-offs in the customer's own terms.
Build the security layer with our security team: resistance to prompt injection, personal data handling, guardrails, audit and data residency.
- Experience with S2S integration, preferably using technologies such as n8n or Temporal, or alternatively MuleSoft, Apache Camel, or Boomi.
- Experience with Go and Python.
- Experience building multi-tenant SaaS solutions.
- Experience building and scaling AI-native products and applications.
- Experience with production-grade GenAI solutions, including RAG pipelines (hybrid retrieval, embeddings) and agentic systems (agent orchestration, tool usage).
- Experience with AI frameworks and tooling such as Pydantic AI, LangChain, and MCP.
- Experience with MCP development and plan-based agentic software development.
- Experience with DevOps and cloud-native infrastructure, including Docker, Kubernetes, CNCF technologies, and CI/CD pipelines.
- Experience with API gateways and API products, preferably with platforms such as Kong or Tyk.
- Preferably experience optimizing AI services for latency and cost.
- Preferably experience with specification-driven development.
- Passionate about and eager to adopt new technologies.
- Up to date with the latest developments in GenAI and AI-based software development.