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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 versatile, supportive setting where your well-being is prioritized and your potential can thrive. The Role Your role: We are seeking a specialist to build secure, AI-ready data pipelines and retrieval architectures that power high-performing enterprise AI agents. In this role, you will ingest, normalize, and enrich complex operational data from sources like ServiceNow, SharePoint, Confluence, Git, and monitoring tools. You will be responsible for designing advanced semantic search, vector storage, and metadata tagging strategies to optimize grounded agent performance, while maintaining strict standards for data quality, lineage, access control, and privacy. What you will do: - Ingestion Pipelines: Build batch and real-time data pipelines for tickets, CMDB records, knowledge bases, and operational logs. - Data Normalization & Quality: Convert raw enterprise data into clean, AI-ready schemas with quality scoring, deduplication, and full metadata tracking. - RAG Architecture: Design chunking, embedding, indexing, and re-ranking strategies to drive high-accuracy vector search for AI agents. - Knowledge Mapping: Construct knowledge graphs and relationship maps across CMDB assets, applications, dependencies, and SLAs. - Data Governance & Security: Enforce strict access controls, sensitive data handling, retention policies, and audit logging for AI retrieval. - Lineage & Compliance: Maintain clear data dictionaries, transformation logic, and evidence logs for security and compliance audits. - Agent Integration: Build retrieval APIs and structured context packages tailored for ticket, SLA, and knowledge-focused AI agents. - Evaluation & Testing: Measure retrieval precision and response freshness while curating golden datasets for continuous testing and tuning. 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 important 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 Technical and Professional Experience - Experience : 5 years in data engineering or platform development, with 2 years dedicated to supporting GenAI, RAG, search, or semantic data products (ITSM/ServiceNow experience preferred). - AI Agent & Data Integration: Proven ability to connect enterprise data sources to platforms like Agent Builder, configure RAG pipelines, and package governed context/APIs for AI workflows. - LLM & RAG Architecture: Strong background in embeddings, hybrid search, re-ranking, knowledge graphs, and vector databases (e.g., Azure AI Search, Pinecone, Elasticsearch). - AI Frameworks: Hands-on familiarity with GenAI stacks and orchestration frameworks, including LangChain, LlamaIndex, LangGraph, and modern LLM endpoints. - Core Data Engineering: Expertise in Python, SQL, ETL/ELT pipelines, data modeling, and orchestration platforms like Databricks, Snowflake, BigQuery, Airflow, or dbt. - Enterprise Systems & ITSM: Practical experience integrating operational data from ServiceNow (CMDB, tickets, SLAs), SharePoint, Confluence, Git, and monitoring tools. - Data Governance & Security: Deep understanding of enterprise data lineage, access controls, auditability, and sensitive data handling for AI usage. - Key Delivery Track Record: Demonstrated ability to deliver AI-ready pipelines, metadata/knowledge models, vector configurations, and retrieval performance evaluation reports. Preferred Technical and Professional Experience - Master Degree & any relevant certification Being You The Kyn in Kyndryl means kinship, which represents the robust bonds we have with each other, our customers and our communities. We focus on ensuring all Kyndryls feel included and we welcome people of all cultures, backgrounds, and experiences. Even if you dont meet every requirement, we encourage you to apply. We believe in growth, and were .
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.