Lead AI Engineer, Forward Deployed

Kyndryl

. Own the technical direction and delivery of major enterprise AI programmes .

Posted 9/15/2026full-timeLondon • United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in architecting and delivering enterprise AI applications, with a strong focus on Python engineering, AI governance, and stakeholder communication. Capable of translating complex AI capabilities into actionable business outcomes while mentoring engineering teams.

Highest-signal resume keywords

Python EngineeringAI GovernanceAgentic AI FrameworksRAG/LLM-Based PipelinesStakeholder Communication

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonFastAPISDLC PracticesRAG PipelinesVector DatabasesRelational DatabasesGraph DatabasesAI Coding CopilotsTensorFlowPyTorch

Soft Skills

Stakeholder CommunicationMentoring

Tools & Technologies

OpenTelemetryPrometheusMCPLangGraphCrewAIAutoGenSemantic KernelOpenAI’s Agents SDKGoogle’s Agent Development KitHugging Face

Industry Keywords

AI ApplicationsData PrivacyResponsible-Use ControlsPolicy-as-CodeOntology Extraction

Tech Stack

Tools & technologies

Neo4jPostgresPrometheusPythonPyTorchSDLCTensorflow

About the role

Key responsibilities & impact

Own the technical direction and delivery of major enterprise AI programmes
Architect agentic systems and generative AI applications for production
Collaborate closely with platform engineering throughout design, build, and deployment
Align enterprise goals with target-state AI architectures for senior stakeholders
Guide the team’s technical direction while remaining hands-on
Design and build agentic AI applications, multi-agent workflows, and supporting frameworks
Build RAG pipelines and integrate LLM APIs, vector databases, and MCP tooling
Process unstructured data into condensed, structured knowledge, including ontology extraction
Write production-grade Python services with FastAPI
Work with relational and graph databases to model and serve data behind AI applications
Support hosting, scaling, MLOps, and LLMOps in collaboration with platform engineering
Define AI governance and responsible-use guardrails
Instrument AI applications for production observability
Translate enterprise requirements into AI solution roadmaps
Capture field learnings and codify reusable agentic patterns
Mentor engineers hands-on
Provide architectural oversight across multidisciplinary workstreams

Requirements

What you’ll need

8–10+ years in software or solution engineering
Track record of shipping AI systems in client-facing engagements
Strong Python engineering with FastAPI
Solid SDLC practices including design, testing, code review, CI/CD, Git, and GitHub
Hands-on experience with agentic AI frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI’s Agents SDK, or Google’s Agent Development Kit
Experience with MCP (Model Context Protocol)
Practical experience building RAG/LLM-based pipelines and working with vector databases
Experience with relational and graph databases such as Postgres and Neo4j
Hands-on familiarity with AI coding copilots such as Claude, Codex, and Cursor
Strong stakeholder communication and ability to translate AI capability into business outcomes
Familiarity with OpenTelemetry and Prometheus
Experience with TensorFlow, PyTorch, and the Hugging Face/open-source AI ecosystem
Experience with image understanding and OCR
Working knowledge of policy-as-code using OPA/Rego
Understanding of LLM governance, data privacy, guardrails, and responsible-use controls
T-shaped profile with deep AI engineering expertise and broad software engineering and technical consulting knowledge
Willingness to travel and work on customer premises as required
Degree in Computer Science, Data Science, Informatics, Engineering, Physics, Mathematics, or a related discipline — or equivalent professional experience

Benefits

Comp & perks

Flexible, supportive environment
Well-being support through Be Well programs covering financial, mental, physical, and social health
Personalized development goals and continuous feedback
Access to cutting-edge learning opportunities
Certifications with Microsoft, Google, and Amazon
Coaching and hands-on experiences
Career-path and professional development tools
Hybrid-friendly culture