Senior AI Engineer (AI Enablement)

Design, build, and operate AI systems for internal functions — agents, assistants, and automations — owning them end-to-end from design through production operation.

Build the shared foundations of the crew's platform: frameworks, knowledge-base pipelines, evaluation tooling, and reusable integrations with company systems.

Work directly with functional champions to understand their workflows, identify high-leverage automation opportunities, and turn them into deployed solutions.

Implement security guardrails and responsible-AI practices in everything shipped: access control, data privacy, auditability, and human-in-the-loop where it matters.

Make systems measurable: instrument adoption, quality, cost, and time savings, and use the data to iterate.

Keep owned systems reliable, observable, and maintainable - including production incident response and RCA.

Contribute to the AI software factory for engineering: rules, skills, and agents that codify Kpler's engineering standards, and AI-assisted development practices across crews.

Provide clarity through documentation, proposals, and runbooks; share knowledge across the crew and company.

Mentor other engineers in the crew and contribute to hiring and onboarding.

5+ years of software engineering experience building and operating production systems end-to-end.

Hands-on experience shipping LLM-powered features, agents, or AI tooling to production (not just prototypes).

Strong integration experience: third-party SaaS APIs, internal services, data pipelines.

Solid grounding in system design, APIs, databases, and application architecture.

Experience working directly with non-technical stakeholders to shape and deliver solutions.

Practical understanding of security and data-privacy constraints around AI systems.

Strong programming in Python and/or TypeScript.

LLM application engineering: model APIs, agent frameworks, prompt/context engineering, RAG and knowledge bases.

API design and systems integration across multiple SaaS platforms and internal systems.

Cloud environments (AWS preferred), CI/CD, containerisation.

Experience with agent frameworks, MCP-style tool interfaces, or multi-agent systems.

Experience with RAG/knowledge-base systems and LLM evaluation/observability tooling.

Experience building internal tools, platform tooling, or developer-experience products.

Cost optimization of LLM workloads.

Full-stack experience for user-facing internal tooling.