• Design, develop, and optimize AI models, including training and fine-tuning for various applications. • Implement and deploy AI solutions on a cloud platform, ensuring scalability and performance. • Utilize Python for data preprocessing, model development, and automation of AI workflows. • Collaborate with the development team to broaden AI capabilities and enhance existing solutions. • Research and experiment with cutting-edge AI technologies, staying ahead of industry advancements. • Optimize and monitor AI models post-deployment, ensuring efficiency, accuracy, and reliability.

• Analyze existing customer business process in tight cooperation with stakeholders • Define “state-to-be” – optimised business processes • Implement improvements via automation and agents to identify automation opportunities • Design and deploy AI agents using platforms such as Zapier, Make.com, n8n, etc • Integrate AI solutions with customer tools (e.g., CRM, ERP, Slack, Notion, Gmail, Hubspot) • Test, monitor, and fine-tune AI agents to ensure reliability and performance • Collaborate closely with product, development, and client teams, customer stakeholders, and process owners

• Conduct qualitative and quantitative research to understand user needs, workflows, and pain points across multiple SaaS products in the portfolio. • Analyze competitive products and the broader AI landscape within our industry to identify patterns, gaps, and emerging best practices. • Partner with Product and Engineering leaders to inventory and assess available first-party data that can be leveraged to train models. • Synthesize insights from user research, competitive analysis, and data exploration into clear opportunity areas for reusable AI agents. • Develop concept briefs, workflows, and problem statements for AI-powered solutions.

• You'll build multi-agent workflows, write system prompts in Italian and English, and ship to production daily. • First month - Learn the system, ship real things: Understand the codebase, how agents coordinate, and how workflows reach production • Pair with Lorenzo on existing workflows - fix bugs, improve prompts, add steps • Ship your first agent or workflow improvement to production • Months 2-3 - Build independently: Design and build new agents with bilingual system prompts and Zod output schemas • Wire AI workflows end-to-end: Mastra steps, API routes, React frontend with real-time streaming • Own prompt performance on your workflows: measure, optimize, iterate • Months 3-6 - Own entire workflows: Architect multi-step pipelines from scratch (parallel execution, concurrency control, error recovery) • Implement document processing capabilities (PDF, DOCX) with confidence scoring • Create tools for agents to interact with databases, S3, and external services • Start shaping what we build next, not just how • After that - You're one of three engineers building the product. You'll have opinions on architecture, push back on ideas that don't make sense, and drive features from 'we should do X' to production. The scope grows as fast as you do.