Lead Engineer - AI Enablement

The Lead AI Engineer will build and lead Kpler's new AI Enablement crew, whose mission is to give every function in the company - from Engineering to People, IT, Customer Success, Legal, Finance, and the commercial teams - the tools, frameworks, and AI agents to work with AI at scale.
This is a hybrid leadership role: you set the technical direction and architecture for Kpler's internal AI tooling, stay hands-on on the most critical components, and manage a small crew (a Senior AI Engineer and an Engineer II to start), with the management scope expected to grow with the crew. 

Your mission is to

Build, lead, and grow the AI Enablement crew — technical direction, delivery, and light people management (1:1s, growth, feedback), evolving toward a fuller management scope as the crew expands.
Own the architecture of Kpler's internal AI tooling: agents and assistants, shared frameworks, knowledge bases, and the integrations that connect them to company systems.
Deliver function-tailored AI solutions in close partnership with functional champions across departments, from discovery and workflow design through production deployment and iteration.
Drive the AI software factory for engineering: codify engineering standards and architectural principles into rules, skills, and agents, and raise the level of AI-assisted development across all crews.
Establish and enforce security guardrails and responsible-AI practices for connecting AI systems to core company systems (access control, data privacy, human-in-the-loop where it matters).
Stay hands-on: design, build, and ship critical components of the platform yourself.
Define and track success metrics for internal AI - adoption and usage, workflows automated, time savings and ROI - and report progress to engineering and executive stakeholders.
Grow AI capability across the company: champions network, sharing sessions, documentation, and enablement.
Contribute to hiring and onboarding for the crew and to Kpler's broader engineering hiring.

This could be a match if you have

Essential:

• 8+ years of software engineering experience, including designing and operating production systems end-to-end.
• Strong programming skills in Python and/or Golang (other JVM languages considered).
• Practical understanding of security, access control, and data-privacy constraints relating to AI systems.
• Experience leading engineering projects as a tech lead, lead engineer, or engineering manager of a small team.
• Hands-on experience shipping LLM-powered products, agents, or AI tooling to production (not just prototypes).
• Integration-heavy engineering background: connecting products to third-party SaaS systems and internal data via APIs.
• Strong stakeholder management with non-technical audiences; able to turn a business workflow into a deployable system.
• Experience with systems integration across commercial SaaS platforms and internal services.
• Exposure to Cloud environments (AWS preferred), CI/CD, and observability for production systems.
 
Desirable:
• Experience building internal tooling, platform, or enablement teams (DevEx, internal products).
• Familiarity with agent frameworks, MCP-style tool interfaces, and multi-agent systems.
• Experience with LLM evaluation, observability, and cost management of AI workloads.
• Experience introducing AI-assisted development practices to engineering organisations.
• Scale-up environment experience; comfort with ambiguity and building a function from scratch.