Kpler - Engineer II (AI Enablement)
Requirements
• 3+ years of professional engineering experience. • Experience building and operating production systems end-to-end (services, APIs, or tooling). • Hands-on experience building with LLMs (features, agents, automations, or serious side projects with production-quality practices). • Understanding of system design, databases/data modelling, and application architecture. • Familiarity with cloud infrastructure and CI/CD pipelines. • Strong problem-solving and collaboration skills, with a user-centric mindset. • Proficiency in Python and/or TypeScript. • LLM application development: model APIs, prompt/context engineering, basic RAG patterns. • API design and data integration across multiple systems and sources. • Database design, SQL, and data modelling. • Familiarity with cloud services (AWS preferred), containerisation (Docker/Kubernetes), and CI/CD practices. • Experience with agent frameworks, MCP-style tool interfaces, RAG, or knowledge-base systems. • Experience integrating third-party SaaS APIs. • Full-stack experience — building web UIs as well as their supporting backend services. • Experience building internal or developer-facing tools. • Exposure to LLM evaluation, observability, or prompt/context engineering.
Responsibilities
• Build, tailor, and operate AI agents and assistants for internal functions, owning features end-to-end from design through deployment and production operation (including testing and CI/CD). • Build and maintain knowledge-base pipelines: collecting, structuring, and keeping current the content that AI systems rely on to be accurate and useful. • Integrate AI systems with company tools and data sources through well-structured, safe APIs and connectors. • Build user-facing tooling and interfaces where the work calls for it, spanning UIs and the backend services behind them. • Instrument what you ship: adoption, quality, and cost metrics that show whether a solution is working. • Gather feedback from internal users, identify friction points, and turn them into concrete improvements. • Keep owned systems reliable, observable, and maintainable; participate in incident response and RCA for owned services. • Provide context and clarity through documentation and runbooks so others understand what's built and why. • Contribute to the crew's shared frameworks and to AI-assisted engineering practices a
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