Overview

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.

Responsibilities

• We are seeking an experienced Data / Retrieval Engineer to design, build and operate enterprise-grade data ingestion and retrieval capabilities for AI agents and knowledge-search platforms.
• The role will focus on transforming enterprise business knowledge into secure, trustworthy and citation-backed context. The engineer will be responsible for improving retrieval quality, grounding, data readiness, latency and operational cost across Retrieval-Augmented Generation and enterprise-search solutions.
• Design and build scalable ingestion pipelines for structured, semi-structured and unstructured data.
• Develop indexing solutions using: Vector retrieval
• Build reranking, grounding and citation-generation mechanisms to improve response accuracy and traceability.
• Implement access-aware retrieval based on users, roles, entitlements and source-system permissions.
• Create retrieval evaluation datasets, including representative queries, expected sources and relevance labels.
• Define and monitor retrieval-quality metrics such as Recall@K, Precision@K, Mean Reciprocal Rank, NDCG, citation accuracy and groundedness.
• Develop monitoring dashboards, alerts and operational runbooks for retrieval quality, data freshness and model or index drift.

Qualifications

• Strong hands-on experience with Python and SQL.
• Production experience implementing Retrieval-Augmented Generation or enterprise-search solutions.
• Strong understanding of: Vector databases
• Enterprise-search platforms
• Embedding models
• Chunking strategies
• Keyword and semantic search
• Hybrid retrieval
• Metadata management Experience building data pipelines, APIs and indexing workflows.
• Knowledge of relational databases, document databases or knowledge-retrieval platforms.
• Strong understanding of data security, privacy, lineage and governance.
• Ability to work with business, data, AI, security and platform-engineering stakeholders.
• Strong analytical, problem-solving and communication skills.
• Preferred Skills Experience with large-scale enterprise knowledge bases and multi-source document ingestion.
• Exposure to MCP-enabled applications or agentic AI platforms.
• Knowledge of search relevance tuning and learning-to-rank techniques.
• Familiarity with financial-services data, regulatory content or S&P-related business information.
• Indicative Technology
• Programming and Data: Python, SQL, APIs, ETL/ELT pipelinesAI and Retrieval: RAG, embeddings, vector search, hybrid search, reranking, grounding, citationsData Platforms: Vector databases, relational databases, document storesSearch: Enterpri .