This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead AI/ML Engineer based in Brazil.

This is a senior technical leadership opportunity focused on defining the architecture and technical direction of enterprise AI and Machine Learning solutions.
You’ll lead the development of scalable AI/ML pipelines and help transform advanced models from prototypes into reliable production systems.
The role combines hands-on engineering with architectural ownership, team mentorship, and strategic decision-making.
You’ll work extensively with LLMs, Generative AI, agentic frameworks, and other advanced models across complex enterprise environments.
The position involves close collaboration with data scientists, software engineers, product leaders, executives, and clients.
You’ll drive MLOps practices that improve model reliability, observability, deployment speed, performance, and cost efficiency.
This is an opportunity to shape AI strategy while delivering production-grade solutions across modern cloud and distributed environments.

• Lead the architectural design and strategic development of high-performance AI and Machine Learning pipelines for complex enterprise initiatives.
• Define technical vision, architecture, and engineering standards for AI/ML solutions across projects.
• Provide technical leadership, mentorship, and guidance to AI/ML engineers and data scientists, promoting engineering excellence and professional growth.
• Deploy, manage, and scale pre-trained and custom AI/ML models, including LLMs, vision models, and Generative AI solutions, in production environments.
• Partner with data scientists and researchers to transform experimental models and prototypes into resilient, production-grade systems.
• Design and optimize AI/ML solutions for latency, scalability, reliability, performance, and cost efficiency.
• Integrate AI/ML platforms with AWS, GCP, and Azure environments, using Docker and Kubernetes for consistent and scalable deployments.
• Establish and improve MLOps practices covering automated testing, CI/CD, model versioning, monitoring, logging, maintenance, and operational governance.
• Build and orchestrate complex Machine Learning pipelines using platforms and tools such as Kubeflow, MLflow, and managed cloud services.
• Work with software engineering teams to integrate AI capabilities into enterprise applications and user workflows in a secure and responsible manner.
• Partner with senior leadership, product managers, solution architects, clients, and other stakeholders to align AI roadmaps with business objectives.
• Drive technical decisions and contribute to project delivery while balancing architectural quality, business requirements, and operational realities.
• Monitor developments in AI/ML research, Generative AI, data engineering, agentic systems, and MLOps to identify opportunities for innovation.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or another relevant quantitative discipline; a Ph.D. is a plus.
• 7–10 years of progressive experience in Machine Learning Engineering, AI-focused software development, or a closely related field.
• Expert knowledge of ADK and other agentic AI frameworks, with demonstrated experience implementing AI agents in production.
• Proven technical leadership experience, including mentoring engineers, influencing architectural decisions, and driving delivery across complex projects.
• Expert-level Python programming skills and strong experience with ML/AI frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience deploying and fine-tuning advanced pre-trained models, particularly LLMs and Generative AI technologies, in low-latency production environments.
• Extensive experience with AWS, GCP, and/or Azure, combined with strong knowledge of Docker and Kubernetes.
• Solid understanding of Data Engineering principles, ETL/ELT processes, and Git-based version control.
• Proven experience building scalable, distributed AI/ML systems and orchestrating complex Machine Learning pipelines.
• Strong knowledge of MLOps practices, including model monitoring, logging, explainability, model versioning, and CI/CD for AI assets.
• Strong understanding of production reliability, scalability, security, and responsible AI practices.
• Excellent communication, stakeholder management, and collaboration skills, with the ability to work effectively with technical and business audiences.
• Ability to translate complex AI capabilities into practical solutions that deliver measurable business value.

• Competitive total rewards package.
• Fully remote work from home with no daily office commute required.
• Flexible work environment designed to support work-life balance.
• Opportunity to collaborate with experienced AI, data, cloud, and engineering professionals.
• Substantial training and professional development allowance.
• Dedicated professional development days and support for training and industry certifications.
• Equipment provided for remote work, including a laptop with a choice of operating system.
• Annual budget to personalize and improve your home workspace.
• Annual wellness budget that can be used for activities such as gym memberships, fitness programs, massages, and other wellness needs.
• Generous paid vacation and sick leave.
• Paid day off for volunteering with a charity of your choice.
• Opportunity to work with advanced AI technologies and complex enterprise transformation initiatives.
• High level of technical ownership and influence over AI architecture, engineering practices, and delivery.
• Accommodation support available upon request during the selection process.
• Employment may be subject to requirements for a background check.