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 Engineer (FDE) with an AI & Agentic Engineering focus (Band 8), you own delivery of AI applications for complex customer engagements, including the agentic systems and generative AI features that go into production in close collaboration with platform engineering. You translate business challenges into technical solutions, build and iterate AI applications, and work hand-in-hand with the platform engineers on the team throughout design, build, and deployment. You’re accountable for what you build, including how well it holds up once people are actually using it.
You operate with significant autonomy, bring deep expertise in agentic AI architectures and applied software engineering, and confidently bridge engineering rigour with business value. You use modern AI development tools and sound engineering practice across the full delivery lifecycle to accelerate learning, improve quality, and deliver measurable impact.
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.
Translate business challenges into agentic AI applications and generative AI use cases
Build and iterate AI applications: agentic workflows and the frameworks that run them, RAG pipelines, and LLM API integrations
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 and MLOps/LLMOps — without owning that infrastructure yourself
Define AI governance and responsible-use guardrails: data privacy boundaries and policy enforcement (OPA/Rego)
Instrument AI applications for observability (OpenTelemetry, Prometheus)
Guide clients through legacy modernisation into AI-enabled workflows, troubleshooting performance and reliability issues
Capture deployment learnings and share best practices to inform core AI platform frameworks
Contribute code, reusable agentic patterns, and feedback to core platform teams
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
5+ years of hands-on software engineering experience, with exposure to AI/ML integration and API design
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) and Docker for building and running AI applications
Hands-on familiarity with modern AI coding copilots (e.g. Claude, Codex, Cursor)
Preferred Skills and Experience
Familiarity with observability instrumentation for AI systems (OpenTelemetry, Prometheus)
Experience with AI/ML frameworks (TensorFlow, PyTorch) and the Hugging Face / open-source AI ecosystem
Experience with image understanding and OCR
Working knowledge of policy-as-code (OPA/Rego) for AI governance and…