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We are currently seeking a Data / Retrieval Engineer to join our team in Hyderabad, Telangana (IN-TG), India (IN).
Job Description: Data / Retrieval Engineer
Position: Senior Individual Contributor Experience: 7+ years Domain: RAG, Enterprise Search, Data Readiness and Retrieval Quality Openings: 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.
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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 pipelines AI and Retrieval: RAG, embeddings, vector search, hybrid search, reranking, grounding, citations Data Platforms: Vector databases, relational databases, document stores Search: Enterprise search, keyword search, semantic search, metadata filtering Governance: Data lineage, access controls, PII management, freshness and retention controls Operations: Monitoring, logging, observability, quality evaluation and drift detection
Key Success Measures
Improved retrieval relevance and citation accuracy.
Reduced hallucination through stronger grounding.
Reliable enforcement of document and user access permissions.
Improved freshness and completeness of indexed enterprise data.
Lower retrieval latency and infrastructure cost.
Effective identification and remediation of retrieval-quality drift.
Production-ready monitoring, governance evidence and operational runbooks.
Candidate Red Flags
Experience limited to prompt engineering without hands-on retrieval implementation.
No experience evaluating retrieval relevance or grounding quality.
Limited understanding of data pipelines, metadata or indexing.
Weak knowledge of security, access control or sensitive-data handling.
Proof-of-concept experience without production deployment, monitoring or operational ownership.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.
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