AI Tech Lead

Let's translate that into activities?

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