Posted 09 June, 2026
AI / DATA ENGINEER
Zensar Technologies
Pune,Maharashtra,IN,411014
Full Time
Reference: 218_649632_146623
The AI/Data Engineer will play a crucial role in designing and implementing data ingestion, processing, and indexing pipelines for our RAG Search application. This role is responsible for ensuring secure and efficient data management, from ingestion to retrieval, and for building advanced retrieval and RAG capabilities. The successful candidate will have strong hands-on experience in data engineering, search technologies, and AI-powered search applications, with a focus on Python and enterprise search platforms.At Zensar, we're "experience-led everything". We are committed to conceptualizing, designing, engineering, marketing, and managing digital solutions and experiences for over 130 leading enterprises. We are a company driven by a bold purpose: Together, we shape experiences for better futures. Whether for our clients, our people, or the world around us, this belief powers everything we do. At the heart of our culture is ONE with Client - a set of four core values that reflect who we are and how we work: One Zensar, Nurturing, Empowering, and Client Focus.
Part of the $4.8 billion RPG Group, we're a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
Part of the $4.8 billion RPG Group, we're a community of 10,000+ innovators across 30+ global locations, including Milpitas, Seattle, Princeton, Cape Town, London, Zurich, Singapore, and Mexico City. Explore Life at Zensar and join us to Grow. Own. Achieve. Learn. to be the best version of yourself.
We believe the best work happens when individuality is celebrated, growth is encouraged, and well-being is prioritized. We are an equal employment opportunity (EEO) and affirmative action employer, committed to creating an inclusive workplace. All qualified applicants will be considered without regard to race, creed, color, ancestry, religion, sex, national origin, citizenship, age, sexual orientation, gender identity, disability, marital status, family medical leave status, or protected veteran status.
- Hands-on experience in data engineering, search technologies, and AI-powered search applications.
- Proficiency in Python and data processing frameworks/libraries.
- Strong experience with enterprise search platforms (e.g., Elasticsearch, OpenSearch).
- Knowledge of index mappings, metadata filters, ranking profiles, and relevance tuning.
- Experience with vector search, hybrid retrieval, and semantic search techniques.
- Ability to design ingestion pipelines that convert enterprise documents into structured Markdown.
- Familiarity with embedding models, re-ranking models, and LLM orchestration frameworks.
- Experience with tool calling, agentic workflows, and AI application orchestration.
- Working knowledge of commercial/open-source LLMs (e.g., Azure OpenAI, OpenAI).
- First-level University degree with a minimum of 5 years of professional experience, including 3 years of hands-on experience in building AI-powered search applications.
- Design and implement scalable data ingestion pipelines for structured, semi-structured, and unstructured content from various sources.
- Develop connectors and ingestion jobs for batch, incremental, and near-real-time indexing.
- Implement mechanisms to track document versions, provenance, permissions, and deletion events.
- Extract and convert content from various file formats into standard markdown for AI readability.
- Design semantic chunking strategies for optimal retrieval quality, including chunk size and overlap.
- Implement metadata extraction, enrichment, and deduplication during ingestion.
- Build hybrid search capabilities combining keyword, semantic vector, and metadata-based retrieval.
- Develop re-ranking pipelines using advanced techniques to improve relevance of results.
- Implement security controls to ensure user access is restricted to authorized information.
- Build RAG pipelines, including agentic workflows, prompt engineering, and response grounding.