We are looking for an AI Forward Deployed Engineer to work directly with customers and internal engineering teams to design, build, deploy, and optimise production-grade AI solutions.

You will own customer-facing technical workstreams from solution discovery and architecture through implementation, deployment, and production go-live. You will translate complex and ambiguous business requirements into practical technical solutions and rapidly build AI-powered applications, agentic workflows, and RAG systems tailored to real-world enterprise use cases.

The role requires strong software engineering skills combined with expertise in Generative AI, LLMs, RAG, AI agents, cloud infrastructure, and production systems. You will also troubleshoot complex customer environments, communicate technical trade-offs to both technical and non-technical stakeholders, and feed recurring customer requirements and implementation patterns back into the product and engineering organisation.

𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝘁 𝗘𝗻𝗴𝗹𝗶𝘀𝗵 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗺𝘂𝘀𝘁!
𝗢𝘄𝗻 customer-facing technical workstreams and integrations from solution design through production go-live.
𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗲 complex and ambiguous business requirements into clearly scoped technical architectures and implementation plans.
𝗗𝗲𝘀𝗶𝗴𝗻, develop, and deploy production-grade applications using Python, TypeScript, and JavaScript.
𝗕𝘂𝗶𝗹𝗱 and optimize Generative AI and autonomous agent solutions for real-world enterprise workflows.
𝗗𝗲𝘀𝗶𝗴𝗻 advanced Retrieval-Augmented Generation (RAG) pipelines, including document chunking, embeddings, retrieval, reranking, and vector database optimization.
𝗗𝗲𝘃𝗲𝗹𝗼𝗽 evaluation frameworks to identify hallucinations, regressions, output drift, quality degradation, and reliability issues.
𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 agentic workflows using frameworks such as LangGraph, LangChain, CrewAI, DSPy, and Model Context Protocol (MCP).
𝗗𝗲𝘃𝗲𝗹𝗼𝗽 and maintain scalable data pipelines using SQL, Spark, Airflow, or equivalent technologies.
𝗗𝗲𝗽𝗹𝗼𝘆 and manage containerized applications using Docker and Kubernetes.
𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 infrastructure-as-code and cloud infrastructure using Terraform and major cloud platforms.
𝗠𝗼𝗻𝗶𝘁𝗼𝗿 AI applications for latency, token consumption, quality, reliability, and behavioral drift using AI observability platforms.
𝗧𝗿𝗼𝘂𝗯𝗹𝗲𝘀𝗵𝗼𝗼𝘁 complex production issues within live customer environments and independently drive issues through resolution.
𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗲 directly with customer engineers, architects, technical leaders, and business stakeholders.
𝗣𝗿𝗲𝘀𝗲𝗻𝘁 technical architectures, trade-offs, limitations, and recommendations to both technical teams and non-technical executives.
𝗖𝗮𝗽𝘁𝘂𝗿𝗲 recurring implementation patterns, customer requirements, and technical insights and feed them back into internal product and engineering teams.