Agentic AI Data Engineer (Greater Noida)

Who We Are At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the worlds leading businesses. We are at the heart of progress; with proven expertise and a continuous flow of AI-powered insight, enabling smarter decisions, faster innovation, and a lasting competitive edge. For our peopleKyndrylsthat means doing purposeful work that powers human progress. Join us and experience a flexible, supportive environment where your well-being is prioritized and your potential can thrive. The Role Your role: As an Agentic AI Data Engineer, you will serve as the core architect behind high-performance data platforms that drive enterprise AI. In this role, you will construct and fine-tune data pipelines, vector databases, and semantic structures, empowering autonomous agents to seamlessly access and analyze complex corporate data. What you will do: - Architect & Build: Design and optimize scalable ETL/ELT pipelines supporting both batch and real-time processing across hybrid cloud environments. - Architect for RAG : Design and scale the pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings. - Scale vector infrastructure : Responsible for the health and performance of our vector databases (e.g., Pinecone, Milvus, or Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops. - Master Data Transformation Engineer semantic layers: Move beyond simple ETL to build knowledge graphs and semantic layers that provide agents with the necessary context to navigate complex infrastructure puzzles. - AI Grounding Infrastructure: Deploy and manage vector databases and semantic layers tailored for high-context AI search and retrieval-augmented generation (RAG). - Integration & APIs: Develop secure, high-throughput API endpoints enabling autonomous agents to seamlessly access structured and unstructured datasets. - Data Governance & Quality: Implement rigorous data quality frameworks, validation rules, and automated testing loops to ensure pristine data delivery. - Collaborative Leadership: Work directly alongside architects, software engineers, and data scientists in rapid innovation cycles to translate business needs into data infrastructure. - Progress to production: Build, deploy, and maintain the CI/CD pipelines for our data infrastructure, ensuring that our context window remains fresh and reliable. Your Future at Kyndryl The career path ahead is full of exciting opportunities to grow and advance within the job family. With dedication and hard work, you can climb the ladder to higher bands, achieving coveted positions such as Principal Engineer or Vice President of Software. These roles not only offer the chance to inspire and innovate, but also bring with them a sense of pride and accomplishment for having reached the pinnacle of your career in the software industry. Who You Are Youre good at what you do and possess the required experience to prove it. However, equally as key you have a growth mindset; keen to drive your own personal and professional development. You are customer-focused someone who prioritizes customer success in their work. And finally, youre open and borderless naturally inclusive in how you work with others. Required Skills & Qualifications - Bachelors degree in Computer Science, Software Engineering, or a related field (or equivalent experience). - 6-8 years in software engineering, AI Data solutions, with experience delivering enterprise-grade implementations and exposure to customer-facing consulting roles. - Expertise in data mining, data storage, and Extract-Transform-Load (ETL) processes. - Experience in data pipelines development and tooling (e.g., Glue, Databricks, Synapse, or Dataproc) and experience with both relational and NoSQL databases (e.g., PostgreSQL, DB2, MongoDB). - Strong programming skills in SQL, Python, and experience with ETL/ELT tools (Airflow, dbt, Kafka). - Expertise in data modeling, distributed systems, and cloud-native platforms (AWS, Azure, GCP). - Familiarity with data governance, lineage, and observability tools (DataHub, Great Expectations). - Experience with vector databases and feature store design for AI/ML workflows. - Ability to troubleshoot complex data issues and optimize performance. Preferred Skills & Qualifications - Background in regulated industries or environments with stringent compliance requirements. - Knowledge of containerization and orchestration (Docker, Kubernetes). - Experience with CI/CD for data workflows, version control, and Agile methodologies. - Professional certification, e.g., Open Certified Technical Specialist with Data Engineering Specialization. - Cloud platform certification, e.g., AWS Certified Data Analytics Specialty, Elastic Certified Engineer, Google Cloud Professional Data Engineer, or Microsoft .