Senior AI Automation Engineer

Accountabilities Business partnership and discovery: Collaborate with stakeholders across Operations, Finance, Marketing, Customer Success, and other functions to understand workflows, identify inefficiencies, and prioritize high-value automation opportunities. Solution design and development: Translate ambiguous business challenges into clear technical requirements and build AI-powered automations, internal tools, and integrations using APIs, scripting, AI-native platforms, and no-code or low-code solutions. Rapid prototyping and delivery: Develop and deploy functional initial solutions quickly, validate their impact with stakeholders, and iteratively improve them based on feedback and business outcomes. AI integration: Connect large language models (LLMs), AI agents, retrieval-augmented generation (RAG), and prompt engineering techniques with existing business systems to automate end-to-end workflows. Production readiness and reliability: Transform successful prototypes into dependable production solutions by implementing monitoring, observability, error handling, documentation, and reusable components. Automation governance and scalability: Determine which solutions can remain lightweight, standalone tools and which require more robust architecture to support wider organizational use. Employee enablement and coaching: Support non-technical colleagues in building their own automations through practical guidance, code and workflow reviews, troubleshooting, workshops, and office hours. Reusable frameworks and platform development: Create shared templates, prompt libraries, integration patterns, and automation components that accelerate adoption and reduce duplicated effort across teams. Cross-functional collaboration: Partner with Engineering and IT on platform decisions, technical standards, and infrastructure that enable secure, reliable, and sustainable automation. Performance measurement and strategic planning: Track business impact through metrics such as time saved, reduced error rates, and shorter processing cycles, while identifying future opportunities and helping shape the automation roadmap. Requirements Proven experience designing, building, and deploying automations, internal tools, or AI-powered workflows that deliver measurable business results. Strong practical knowledge of modern AI technologies, including LLMs, AI agents, retrieval-augmented generation (RAG), and prompt engineering. Experience integrating APIs, scripts, existing business systems, and no-code or low-code automation platforms. A business-oriented mindset, with the ability to understand user needs, ask insightful questions, clarify ambiguous requirements, and prioritize solutions according to their potential impact. Demonstrated ability to move quickly from concept to working prototype, validate assumptions, and refine solutions without overengineering. Sound technical judgment to distinguish between quick, one-off automations and solutions that require production-grade reliability, monitoring, and scalability. Strong written and verbal communication skills, including the ability to explain technical concepts and trade-offs to non-technical audiences. Experience mentoring, training, or supporting non-technical colleagues who build their own tools and automations is an asset. Background in internal tools, Revenue Operations (RevOps), Business Operations (BizOps), or applied AI is considered an advantage. Confidence working with data, including extracting, cleaning, interpreting, and using it to support business decisions. Strong analytical, organizational, and problem-solving skills, combined with a proactive, resourceful, and results-driven approach. Ability to collaborate across diverse teams, adapt to changing priorities, take ownership of deliverables, and communicate progress and challenges transparently. A growth-oriented mindset, with curiosity, empathy, resilience, and a commitment to continuous improvement and inclusive teamwork. Benefit