AI Data Architect

Who We Are

At Kyndryl, we run and reimagine the mission-critical technology systems that drive advantage for the world’s 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 people—Kyndryls—that 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:

The ideal candidate will define and lead the target-state AI data architecture across all lifecycle stages—from initial transition and Day 1 operations to ongoing BAU support and continuous optimization. In this role, you will architect mechanisms to ingest, prepare, and structure complex enterprise data sources (including ServiceNow, CMDB, knowledge bases, code repositories, monitoring feeds, and operational logs) for seamless AI agent consumption. You will design governed Retrieval-Augmented Generation (RAG) pipelines, semantic search models, vector indexing strategies, knowledge graphs, and scalable data products tailored for agentic workflows. Crucially, you will enforce strict data governance, ensuring all data accessed by AI agents remains highly accurate, permission-aware, traceable, auditable, and compliant with customer privacy and data boundary standards. Additionally, you will provide cross-functional architectural leadership to AI Data Engineers, Agent Engineers, Forward Deploy Engineers, Solution Architects, and customer stakeholders to align data strategy with operational objectives.

What you will do:

• End-to-End Data Architecture: Design blueprints and canonical models for ingesting, enriching, and serving operational enterprise data (ServiceNow, CMDB, Git, SharePoint, SOPs) to AI agents.
• Agentic Data Enablement: Architect governed APIs, retrieval services, context packages, and structured output schemas for multi-agent workflows and Agent Builder platforms.
• RAG & Search Strategy: Define enterprise RAG models, chunking/indexing policies, hybrid search patterns, and vector database topologies (Azure AI Search, Pinecone, Milvus, pgvector).
• Data Quality & Benchmarking: Establish quality thresholds, retrieval precision/recall metrics, and golden evaluation datasets to measure drift and optimize response accuracy.
• Security & Access Governance: Enforce Zero Trust data access, RBAC/ABAC, data boundaries, privacy masking, and lineage logging across all AI retrieval and model interaction layers.
• Cloud & Platform Blueprinting: Architect scalable data pipelines, storage, and lakehouse/vector infrastructure across Azure, GCP, AWS, Databricks, or Snowflake landing zones.
• AMS Domain Modeling: Map ITSM and Application Management Services operational workflows (incidents, CMDB, SLAs) into high-value data products for automated ticket analysis and runbook generation.
• Engineering Leadership: Provide architectural direction to Forward Deploy and Data Engineers while managing standards for performance, cost, security, and production readiness.

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

You’re 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, you’re open and borderless – naturally inclusive in how you work with others.

Required Technical and Professional Experience

• AI & Agentic Data Architecture: 10+ years in enterprise data/platform architecture, with 3+ years specifically building GenAI, RAG, and AI agent data models using frameworks like LangChain, LlamaIndex, and AutoGen.
• LLM, RAG & Vector Platforms: Expertise in context management, grounding, chunking, and hybrid search across vector engines (Azure AI Searc…