• Design, build and ship production software that brings AI capabilities to OE’s production teams including agents, retrieval-augmented generation (RAG) pipelines, conversational interfaces, internal tools and client-facing features.
• Embed with delivery teams to understand their domain, identify high-leverage opportunities for AI, and translate them into working software that genuinely improves how they work.
• Collaborate with Product Owners to establish business requirements and develop them into tangible deliverables, adapting based on the business needs.
• Build and operate Model Context Protocol (MCP) servers and other integrations that connect frontier models to OE’s data, systems and workflows, including Salesforce, Microsoft 365, Azure and our proprietary economic forecasts and datasets.
• Take ideas from prototype to production end-to-end, owning the full software lifecycle: design, code, testing, CI/CD, observability, cost monitoring, evaluation and safe rollout.
• Apply solid software engineering fundamentals, clean code, testing, modularity, performance, security, to AI-enabled systems, where quality and reliability matter just as much as anywhere else.
• Experiment with new models, frameworks and techniques as they emerge, and form a strong, well-evidenced point of view on what is hype and what is worth betting on.
• Design evaluation harnesses and feedback loops so we can measure whether our features are actually working, and keep improving them as models and data evolve.
• Help shape OE’s internal AI tooling, including Claude skills, internal MCP servers, shared libraries and the wider AI application framework.
• Embed AI safety, security and responsible-use practices into everything you build, including data handling, prompt-injection defences, and alignment with ISO 27001 controls and OE’s AI Acceptable Use Policy.
• Contribute to internal AI enablement, share what you learn, run brown-bag sessions, write up patterns and help colleagues across OE raise their own AI fluency.
• Stay close to the frontier: track research, model releases and ecosystem developments, and bring back what matters for OE.

Key Working Relationships

• Software Engineering / Product Owners
• IT Operations
• Business Development / Marketing

• Frontier model providers (e.g. Anthropic)
• Cloud providers
• AI tooling and SaaS vendors

• 2+ years of professional software engineering experience, shipping production code in modern cloud environments.
• Software engineering fundamentals, clean code, testing, version control, code review, modular design and a feel for when to be pragmatic versus principled.
• Strong proficiency in C#/TypeScript, with comfort working across the stack from APIs through to lightweight user interfaces.
• Experience designing, deploying and operating cloud-native services on Azure and/or AWS, including CI/CD and infrastructure-as-code.
• Demonstrable hands-on experience building with large language models, including prompting, function and tool calling, retrieval-augmented generation, and agent design.
• Practical experience integrating with model APIs (e.g. Anthropic, OpenAI, Azure OpenAI), with a clear understanding of cost, latency, context windows and rate limits.
• Familiarity with vector databases, embeddings and modern retrieval techniques, including semantic search, hybrid search and reranking.
• Knowledge of LLM evaluation: Understand the differences between LLM testing vs traditional software.
• Comfort working across multiple teams and domains, quickly building enough understanding of someone else’s problem to develop something useful.
• A strong bias for shipping prototypes, you would rather get something working in front of users than polish it indefinitely.
• Excellent communication skills, with the ability to explain trade-offs clearly to engineers, domain experts and senior stakeholders alike.
• Genuine intellectual curiosity, a tinkerer’s instinct, and authentic excitement about where AI is going.

• Experience using agentic coding tools such as Codex, GitHub Copilot and/or Claude code to ship production software.
• Hands-on experience with the Model Context Protocol (MCP), tool and function calling, or agent frameworks (e.g. Microsoft Agent Framework, LangChain or custom orchestration).
• Experience building software that works with content, publishing or knowledge-management workflows (e.g. CMS integrations, editorial tooling, document processing).
• Experience working with data engineering teams or building on top of modern data platforms (e.g. Snowflake, ClickHouse, Databricks, BigQuery).
• Experience deploying AI features into Salesforce, Microsoft 365 or other enterprise SaaS contexts.
• Exposure to fine-tuning, distillation, embeddings training or other model-customisation techniques.
• Background in machine learning, NLP, data science or applied research.
• Experience with prompt engineering at scale, including prompt versioning and structured prompt management.
• Familiarity with AI safety and responsible-AI frameworks.
• Domain interest in economics, forecasting, financial services or research-led businesses.
• Open-source contributions, side projects, blog posts or other evidence that you build for enjoyment.
• Degree in Computer Science, Mathematics, Statistics or equivalent practical experience.

• You ship. Working AI-enabled software is landing in production regularly and being used by real people across OE’s platforms and by our clients.
• Teams across the business see measurable productivity gains from the tools you build, research drafted faster, data more discoverable, model outputs more accessible, repetitive work automated.
• You are trusted by developers alike, people across those teams come to you with for advice.
• OE’s AI platform, including its MCP servers, skills, shared libraries and evaluation harnesses, is well-engineered, observable and easy to extend.
• You become the go-to person across the business for “can we do this with AI?”, trusted to give an honest, well-informed answer.
• New ideas reach a usable prototype in days, not months, and the path from prototype to production is short and safe.
• Colleagues across OE are more confident, capable and ambitious with AI because of how you work alongside them.
• Oxford Economics’ AI capability is visibly ahead of the curve for a firm of our size and sector.