This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We're seeking a highly motivated and technically skilled AI Modeling Lead to join our Automation and AI team. This role requires strong machine learning and data science expertise, excellent coding practices, and the ability to translate real-world business challenges into impactful AI solutions. The ideal candidate is a hands-on contributor who thrives in a fast-paced, purpose-driven environment while operating within the core values and principles of the organization. - Lead the development and implementation of advanced machine learning models for strategic use cases. - Define modeling standards, validation strategies, and performance benchmarks. - Collaborate with business and technical teams to align models with operational goals. - Mentor and guide modeling team members, sharing best practices and driving technical growth. - Ensure model transparency, fairness, and governance compliance. Qualifications - Master's degree in computer science, Machine Learning, or a related field. - 7+ years of experience in AI/ML modeling. - Experience in technical leadership and/or cross-functional leadership. Requirements - Professional Degree (Ph.D) in Computer Science, Machine Learning, or a related field. - Industry knowledge/experience in finance or insurance. - Proven track record of delivering high-impact models to production. - Familiarity with responsible AI practices and regulatory considerations. - Expertise in predictive modeling, NLP, deep learning, and LLMs. Benefits - Work Solution: Hybrid (Tuesday-Thursday) or Remote (USA). - Relocation Eligibility: Available.

• Diseñar, desarrollar e implementar agentes conversacionales inteligentes utilizando Vertex AI Agent Builder. • Integrar LLMs de Google Cloud y de terceros en agentes conversacionales para mejorar la comprensión del lenguaje natural y la generación de respuestas. • Implementar técnicas de RAG para conectar agentes conversacionales con fuentes de datos externas y proporcionar respuestas precisas y contextualizadas. • Utilizar LangChain y LangGraph para construir flujos de conversación complejos y orquestar la interacción entre diferentes componentes de IA. • Optimizar el rendimiento y la escalabilidad de los agentes conversacionales en Google Cloud. • Aplicar las mejores prácticas de ingeniería de prompts para mejorar la calidad y la eficiencia de las interacciones con LLMs. • Colaborar con diseñadores de UX y otros ingenieros para crear experiencias de usuario atractivas e intuitivas. • Mantenerse al día con las últimas investigaciones y tendencias en IA generativa.

• Define and lead end-to-end AI architecture for complex ecosystems, balancing technical depth with business outcomes. • Translate business strategy into scalable AI technology solutions through discovery workshops, assessments, and roadmaps. • Act as a technical sponsor for CI&T’s most strategic accounts, working closely with C-level clients and non-technical stakeholders. • Lead architectural design, technology selection, and proofs of concept for critical AI platforms and innovation programs. • Govern technical quality across delivery squads, driving adherence to security, performance, scalability, and privacy standards. • Support pre-sales, proposal development, RFP responses, and technical visioning with account teams and clients. • Coach and mentor senior architects and technical leads, shaping technical career development paths.

• Build AI-Powered Developer Tools: Design, implement, and ship production-ready AI agents and tools that accelerate developer productivity such as code review and refactoring assistants, automated test generators, local environment setup tools, deployment pipeline diagnostic agents, and agents that simplify on-call tasks when handling incidents • Implement LLM Integrations: Build robust, production-grade integrations with LLM APIs (OpenAI, Anthropic, etc.) such as prompt engineering, response parsing, error handling, rate limiting, cost management, and performance optimization • Develop Agent Orchestration Systems: Create agent frameworks and orchestration systems that enable complex multi-step workflows, tool calling, context management, and agent-to-agent communication • Contribute to Platform Infrastructure: Build self-service platform capabilities that enable teams across Docker to rapidly deploy and operate their own AI developer tools such as deployment pipelines, observability integration, security controls, and operational tooling • Drive Adoption of AI-Native Development: Build tools and programs that accelerate adoption of AI developer tools such as Claude Code, Cursor, and Warp across Docker's engineering organization • Ensure Production Quality: Write well-tested code with strong test coverage (unit, integration, end-to-end); establish monitoring, alerting, and operational excellence for AI systems • Collaborate Cross-Functionally: Partner with Principal Engineer and Senior Engineers on architecture, work with product and design teams on features and UX, and collaborate with platform teams (Infrastructure, Security, Data) on integrations; build effective partnerships across multiple teams • Act as Technical Resource: Help teammates solve problems and share knowledge through code reviews and technical discussions • Participate in Operations: Take part in on-call rotation for AI developer tools; respond to incidents, debug production issues, and drive continuous improvement of system reliability • Document and Share: Create clear technical documentation for features you build; share patterns and learnings with the team • Measure and Iterate: Instrument AI tools to measure adoption, effectiveness, and developer productivity impact; iterate based on data and user feedback to continuously improve developer experience