Open SourceSDLCAIArtificial IntelligenceGenerative AIClaudeGeminiRAGCI/CDMentoringCommunication
About this role
Role Overview
You will be responsible for leading the evolution of an autonomous development pipeline accelerated by artificial intelligence and coordinated by humans at a major educational institution.
Act as the technical reference for defining, implementing and orchestrating AI agents, tools and processes, ensuring efficiency, security, governance and scalability across the digital product development lifecycle.
Design and orchestrate AI agent architectures (multi-agent systems, autonomous workflows, internal copilots), integrating them into the existing development pipeline (SDLC, CI/CD, monitoring, observability).
Define and evolve generative AI usage standards (prompting guidelines, templates, RAG, data security and privacy policies, prompt versioning, etc.).
Establish effectiveness metrics for the autonomous development pipeline (e.g., lead time, throughput, onboarding time, error rates, adherence to standards, team adoption of agents) and drive continuous improvement plans.
Lead proofs of concept (PoCs) and experiments with new AI technologies and development tools, evaluating ROI, risks, governance and productivity impact.
Ensure the pipeline complies with compliance requirements, data privacy regulations (LGPD) and the institution's internal policies.
Conduct workshops, tech talks and internal training on software engineering, AI applied to development and automation best practices.
Serve as a bridge between business and technology, translating strategic needs into concrete technical solutions and prioritizing high-impact initiatives.
Contribute directly to critical code development, especially in more complex or strategic initiatives, acting as a hands-on technical reference.
Structure observability and feedback mechanisms for AI agents (telemetry, logging, tracing, response quality assessment, prompt/policy retraining).
Support and accelerate development teams by delivering enabling and accelerating solutions.
Provide technical support, mentoring and training to raise the teams' technical excellence.
Perform technical reviews, evaluating proposed solutions for compliance with architectural guidelines and best practices.
Requirements
Practical experience with prompt engineering and designing multi-agent flows (division of roles among agents, coordination, handoff to humans).
Knowledge of RAG (Retrieval-Augmented Generation), vector/embedding usage and model integration with internal knowledge bases (documentation, code, wiki, etc.).
Understanding of limitations, risks and biases of generative AI models, with the ability to design safeguards (guardrails, response validation, filters, etc.).
Proficiency in the applicability and limitations of major market models (Claude, GPT, Gemini).