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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. We are currently seeking a Data / Retrieval Engineer to join our team in Noida/Gurgaon, Uttar Pradesh (IN-UP), India (IN).Job Description: Data / Retrieval Engineer Position: Senior Individual ContributorExperience: 7+ yearsDomain: RAG, Enterprise Search, Data Readiness and Retrieval QualityOpenings: 1 Role Overview 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. Key Responsibilities Own the data ingestion and retrieval ecosystem supporting enterprise AI agents. Design and build scalable ingestion pipelines for structured, semi-structured and unstructured data. Develop indexing solutions using: Vector retrieval Keyword search Hybrid retrieval Semantic search Implement document chunking, embedding generation, metadata enrichment and indexing strategies. Build reranking, grounding and citation-generation mechanisms to improve response accuracy and traceability. Convert business documents and knowledge assets into reliable, contextual and reusable data products. Implement access-aware retrieval based on users, roles, entitlements and source-system permissions. Apply metadata filtering, document lineage, freshness controls and source-level traceability. Establish appropriate controls for: Personally Identifiable Information Sensitive and confidential data Data retention Security and governance evidence Partner with AI-agent, MCP, application and platform engineers to improve retrieval relevance, grounding, latency and infrastructure cost. 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. Troubleshoot production issues related to ingestion failures, missing documents, stale indexes, access-control leakage and poor retrieval relevance. Optimise retrieval pipelines for scalability, availability, observability and performance. Required Skills and Experience 7+ years of experience in data engineering, backend engineering, search engineering, machine learning engineering or knowledge-platform development. 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 Reranking Metadata management Experience building data pipelines, APIs and indexing workflows. Knowledge of relational databases, document databases or knowledge-retrieval platforms. Experience implementing role-based or attribute-based access controls within retrieval systems. Strong understanding of data security, privacy, lineage and governance. Experience with monitoring, logging, tracing and production observability. 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. Experience with document parsing, OCR, table extraction and content normalisation. Knowledge of search relevance tuning and learning-to-rank techniques. Experience with cloud-based data and AI platforms. Familiarity with financial-services data, regulatory content or S&P-related business information. Experience designing governance evidence, audit trails and data-quality controls. Indicative Technology Exposure 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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.