Senior Software Engineer – Data Science, AI

Euromonitor International

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

Posted 10/13/2026full-timeBengaluru • IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in Data Science and Software Engineering, with a strong focus on Machine Learning, Generative AI, and backend service design. Capable of building reusable AI components and ensuring production quality through observability and rigorous testing practices.

Highest-signal resume keywords

Machine Learning FundamentalsGenerative AI EngineeringBackend Engineering CompetenceAPI and Service DesignStatistical Rigor

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Hard Skills

Data ScienceMachine LearningRankingRecommendationNLPPythonC#JavaGoTypeScript

Soft Skills

CollaborationMentoringCommunication

Tools & Technologies

KubernetesElasticsearchOpenSearchAzure AI SearchPgvectorLangGraphLangChainLlamaIndexSemantic KernelMLflow

Industry Keywords

Information RetrievalSearch RelevanceMarket ResearchPublishingInformation Services

Tech Stack

Tools & technologies

AWSAzureCloudElasticSearchGoogle Cloud PlatformJavaKubernetesOpen SourcePythonPyTorchScikit-LearnTensorflowTypeScriptGo

About the role

Key responsibilities & impact

Develop reusable ranking, personalisation and recommendation components from experimentation through shared services with measurable quality baselines
Build and operate an evaluation harness as a shared service, including golden dataset curation, LLM-as-judge pipelines, regression gates in CI and quality metrics
Advance shared retrieval across structured and unstructured data through chunking and indexing strategies, hybrid search, reranking and embedding lifecycle management
Apply statistical rigour through experiment design, judge calibration and interpretation of quality deltas
Design and build backend services and APIs for retrieval, ranking, evaluation and agentic AI platform components
Contribute to the agent framework, skills and tool registry with clean SDK surfaces, backward compatibility and disciplined abstractions
Embed observability, cost and token telemetry, quality gates and Responsible AI controls in production components
Onboard consuming teams through reference implementations, documentation and paved-road patterns
Collaborate with Engineering, Architecture and Data Platform teams on cloud-native platform alignment
Improve engineering practices through design reviews, testing standards and mentoring
Report to the Technical Lead and work from the Bengaluru-based AI and Data Science team

Requirements

What you’ll need

7+ years across Data Science and software engineering
Deep applied Data Science: Machine Learning fundamentals, ranking, recommendation or NLP experience, and statistical grounding to design and interpret evaluations
Proven track record of building AI and Data Science functionality as reusable services, libraries or components adopted by multiple teams
Hands-on depth in Generative AI engineering: RAG and retrieval systems; LLM orchestration frameworks such as LangGraph, LangChain, LlamaIndex or Semantic Kernel
Experience with prompt and evaluation tooling
Experience with vector or hybrid search such as Elasticsearch, OpenSearch, Azure AI Search, pgvector or a dedicated vector database
Strong backend engineering competence in API and service design, data pipelines, and production operations on Azure, AWS or GCP
Container orchestration with Kubernetes
Strong Python plus working competence in at least one further backend language such as C#, Java, Go or TypeScript
Production rigour including observability and tracing for LLM workloads, automated testing, CI/CD and cost awareness
Ability to explain technical approaches, trade-offs, limitations and results clearly to engineers, product owners and other stakeholders
Experience with MCP or tool calling protocols, agent runtimes, or LLM gateway patterns such as routing, caching and fallback
LLM-as-judge calibration, golden dataset methodology or online experimentation experience
Experience with evaluation frameworks such as Ragas, DeepEval or promptfoo
Model development experience with PyTorch, TensorFlow or scikit-learn, including training or fine tuning beyond API-level usage of foundation models
Exposure to AutoGen, CrewAI, OpenAI Agents SDK, MLflow, Azure Machine Learning, SageMaker or Vertex AI
Contribution to internal platforms, open source libraries or SDKs with real external consumers
Experience in information retrieval or search relevance at scale
Background in market research, publishing, information services or another accuracy-sensitive domain

Benefits

Comp & perks

Flexible hours
Regular social events, including after-work meetups, summer and Christmas parties
Two volunteering days annually
Donation amounts for new starters and support for local and international charities through various initiatives
Competitive salaries
Private health insurance
Generous holiday allowances
Extensive training and development
Promotion from within and across departments
Rewarding talent
Equal employment opportunity and non-discrimination