Senior AI Engineer (AI Enablement)

Job description

At Kpler, we are dedicated to helping our clients navigate complex markets with ease. By simplifying global trade information and providing valuable insights, we empower organisations to make informed decisions in commodities, energy, and maritime sectors. Since our founding in 2014, we have focused on delivering top-tier intelligence through user-friendly platforms. Our team of over 850 experts from 69 countries works tirelessly to transform intricate data into actionable strategies, ensuring our clients stay ahead in a dynamic market landscape. Join us to leverage cutting-edge innovation for impactful results and experience unparalleled support on your journey to success. *The Senior AI Engineer will join Kpler's new AI Enablement crew, whose mission is to give every function in the company the tools, frameworks, and AI agents to work with AI at scale.* *A senior individual contributor role: you design, build, and operate production-grade AI systems - agents and assistants, shared frameworks, knowledge bases, and the integrations that connect them to company systems - and own solutions end-to-end, from understanding a function's workflow with its champions through deployment, measurement, and iteration.* *You bring engineering rigour to a fast-moving space: the systems you ship must be reliable, observable, safe, and worth their cost.* *You will also help raise the bar for AI-assisted engineering across Kpler and mentor less experienced engineers in the crew. To begin with, the emphasis will be on delivering Kpler's company-wide AI adoption plan; the scope is expected to broaden as internal AI usage matures.* ### Key Responsibilities - 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. ### Experience & Background **Essential:** - 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. **Desirable:** - 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.