AI Tech Lead

Are you ready to translate activities?

• Translate product vision and business needs into scalable technical solutions, transforming ideas and canvases into clear, estimable, and executable plans;
• Lead the definition of technical architecture for the AI platform, ensuring scalability, reliability, maintainability, and sustainable evolution over time;
• Serve as a technical reference point for the team, supporting complex decisions, unlocking critical challenges, and elevating the level of quality of deliveries;
• Develop code actively in strategic features, proof-of-concepts, critical integrations, and high-impact technical initiatives;
• Design, implement, and evolve AI platforms in production, including agent workflows, LLM-based applications, and preparation for MCP (Model Context Protocol) servers;
• Define and sustain engineering standards, best practices of development, code review, versioning, testing, CI/CD, and quality gates;
• Conduct technical and architectural reviews, evaluating trade-offs between innovation, stability, cost, and time-to-delivery;
• Work closely with Product Managers and stakeholders to evaluate technical feasibility, scope, and prioritization, supporting strategic decisions on the roadmap;
• Manage technical expectations of stakeholders, communicating risks, limitations, dependencies, and impacts clearly and objectively;
• Lead initiatives for modernizing the platform, addressing technical debt, and preparing the foundation for future growth;
• Ensure continuity of technical knowledge, documenting architectural decisions and supporting the transition from a contractor-dependent model to a team-oriented FTEs;
• Mentor engineers and data scientists in software engineering best practices, going beyond traditional data science scope;
• Work collaboratively with multidisciplinary teams, including agronomists, data scientists, engineers, and business stakeholders, translating domain needs into technical solutions;
• Evaluate, test, and integrate new technologies, frameworks, and AI approaches in a pragmatic and value-oriented manner;
• Monitor performance, reliability, and quality of solutions in production, promoting continuous improvements and optimizations;
• Serve as a bridge between AI research, software engineering, and digital product delivery with real-world impact.