Senior Software Engineer

Applied Data Science and AI 

• Develop reusable ranking, personalisation and recommendation components, taking them from experimentation through to shared services with measurable quality baselines. 

• Build and operate the evaluation harness as a shared service: golden dataset curation, LLM-as-judge pipelines, regression gates in CI, and quality metrics that consuming teams adopt. 

• Advance the shared retrieval capability across structured and unstructured data: chunking and indexing strategies, hybrid search, reranking and embedding lifecycle. 

• Bring statistical rigour to the platform: experiment design, judge calibration and the interpretation of quality deltas, so decisions about models and components rest on evidence. 

Reusable Platform Services 

• Design and build backend services and APIs for the AI platform (retrieval, ranking, evaluation and agentic components) as versioned, documented, multi-consumer services rather than product embedded code. 

• Contribute to the agent framework and skills and tool registry, with clean SDK surfaces, backward compatibility and abstraction discipline suitable for consumption by multiple product teams. 

• Build for production from the start: observability, cost and token telemetry, quality gates and Responsible AI controls embedded in every component. 

Collaboration and Enablement 

• Onboard consuming teams onto platform components through reference implementations, documentation and paved road patterns; treat adoption as a first class outcome of every piece of work. 

• Work with Engineering, Architecture and Data Platform teams to keep platform services aligned with Euromonitor's cloud native architecture. 

• Raise the bar on engineering practice within the team through design reviews, testing standards and mentoring as the team grows. 

Requirements

What we’re looking for

Essential 

• 7+ years across Data Science and software engineering, with deep applied Data Science: Machine Learning fundamentals, ranking, recommendation or NLP experience, and the statistical grounding to design and interpret evaluations. 

• A proven track record of building AI and Data Science functionality as reusable services, libraries or components adopted by multiple teams, with evidence of that adoption, rather than solely features inside a single product. 

• Hands-on depth in modern Generative AI engineering: RAG and retrieval systems; LLM orchestration frameworks such as LangGraph, LangChain, LlamaIndex or Semantic Kernel (LangGraph and LangChain preferred); prompt and evaluation tooling; and vector or hybrid search such as Elasticsearch, OpenSearch, Azure AI Search, pgvector or a dedicated vector database (Elasticsearch preferred). 

• Strong backend engineering competence: API and service design, data pipelines, and production operations on a major cloud platform such as Azure, AWS or GCP, with container orchestration on Kubernetes (Azure and AKS preferred). 

• Strong Python, plus working competence in at least one further backend language such as C#, Java, Go or TypeScript (C# and .NET preferred, reflecting the services you will integrate with). 

• Evidence of production rigour: observability and tracing for LLM workloads (OpenTelemetry, LangSmith, Langfuse or similar), automated testing, CI/CD (Azure DevOps or GitHub Actions preferred) and cost awareness. 

• The ability to explain technical approaches, trade-offs, limitations and results clearly to engineers, product owners and other stakeholders; adoption of platform components depends on it.