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
As a Forward Deployed Lead / Principal Engineer (FDE) with an AI & Agentic Engineering focus (Band 9), you own the technical direction and delivery of major enterprise AI programmes — architecting the agentic systems and generative AI applications that go into production in close collaboration with platform engineering. You align enterprise goals with target-state AI architectures for senior stakeholders, guide the team’s technical direction while staying close to the work, and work hand-in-hand with the platform engineers on the team throughout design, build, and deployment.
You combine deep expertise in agentic AI systems, RAG, and applied software engineering with strong consulting acumen. You establish AI governance frameworks, champion sound engineering practice across the AI delivery lifecycle, and act as a go-to technical reference on complex engagements.
What You Will Do
Depending on the engagement, you’ll draw on some of the below more than others — not every bullet applies to every project.
Design and build agentic AI applications and multi-agent workflows, along with the frameworks that run them, for enterprise use cases
Build RAG pipelines and integrate LLM APIs, vector databases, and MCP (Model Context Protocol) tooling
Process unstructured data into condensed, structured knowledge, including ontology extraction
Write production-grade Python services (FastAPI) with solid engineering practices: design, testing, code review, CI/CD
Work with relational and graph databases (e.g. Postgres, Neo4j) to model and serve data behind AI applications
Work closely with platform engineering throughout deployment — hosting, scaling, and MLOps/LLMOps — without owning that infrastructure yourself
Define AI governance and responsible-use guardrails: data privacy boundaries, LLM governance, and policy enforcement (OPA/Rego)
Instrument AI applications for observability (OpenTelemetry, Prometheus) so behaviour and cost stay visible in production
Translate enterprise requirements into AI solution roadmaps for senior stakeholders
Capture field learnings, codify reusable agentic patterns, and mentor engineers hands-on
Provide architectural oversight across multi-disciplinary workstreams, staying close enough to unblock the team directly
Who You Are
You don’t need to tick every box below to apply — this reflects the breadth of the role, not a strict checklist.
Required Skills and Experience
8–10+ years in software or solution engineering, with a track record of shipping AI systems in client-facing engagements
Strong Python engineering (FastAPI), with solid SDLC practices: design, testing, code review, CI/CD, Git & GitHub
Hands-on experience with agentic AI frameworks (examples include LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI’s Agents SDK, and Google’s Agent Development Kit) and MCP (Model Context Protocol)
Practical experience building RAG/LLM-based pipelines and working with vector databases
Experience with relational and graph databases (e.g. Postgres, Neo4j) for data modelling behind AI applications
Hands-on familiarity with modern AI coding copilots (e.g. Claude, Codex, Cursor)
Strong stakeholder communication, translating AI capability into business outcomes for senior stakeholders
Preferred Skills and Experience
Familiarity with observability instrumentation for AI systems (OpenTelemetry, Prometheus)
Experience with AI/ML frameworks (TensorFlow, PyTorch) and the Hugging Face /…