Senior Software Engineer - Data Science & AI

About Euromonitor Euromonitor International leads the world in data analytics and research into markets, industries, economies and consumers. We provide truly global insight and data on thousands of products and services; we are the first destination for organisations seeking growth. With our guidance, our clients can make bold, strategic decisions with confidence.

Overview Of The Role Passport, Euromonitor’s flagship platform, has just been rebuilt from the ground up. It is the largest technology investment in the company’s history, and AI now sits at the core of the system.

Euromonitor is establishing an AI and Data Science team in India to lead this work. The remit is a framework driven, platform centric approach: the team builds the AI platform, reusable agents, skills and tooling that product teams use to engineer AI and Data Science capability into Passport and other customer offerings, including new products built on AI and agentic flows.

Based in Bengaluru and reporting to the Technical lead, this role suits a senior engineer whose centre of gravity is applied Data Science and AI (ranking, retrieval and evaluation), combined with the backend software engineering competence to ship that work as reusable, production grade services that other teams build on. Reusability is the defining measure of the role: success is components adopted across product teams, not features shipped into a single product.

What You’ll Do 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 furt…