Staff AI

Accountabilities

• • Contribute to the AI vision and roadmap, bringing strong technical insight to product and business decisions.
  • Architect and lead the development of AI systems in production, from initial experimentation through scalable deployment.
  • Make strategic build-versus-buy-versus-fine-tune decisions for AI models and solutions.
  • Integrate LLMs, foundation models, and generative AI capabilities into digital products.
  • Establish technical standards, engineering practices, and a strong AI Engineering culture across the team.
  • Partner closely with Product Managers, Designers, and Software Engineers to embed AI capabilities into products and user experiences.
  • Mentor and develop AI/ML engineers, helping raise technical expertise and engineering quality across the organization.
  • Research and assess emerging technologies, including LLMs, diffusion models, and multimodal AI, and recommend strategic adoption when appropriate.
  • Lead initiatives related to AI governance, model evaluation, quality, bias mitigation, and responsible AI practices.
  • Participate in technical hiring and contribute to building a high-performing AI engineering team.

  Requirements

  • Advanced professional experience in Artificial Intelligence, Machine Learning, or AI-focused Software Engineering, with a strong track record of delivering production-grade solutions.
  • Advanced Python development skills, with experience building robust production systems rather than working exclusively in notebooks.
  • Hands-on experience with Generative AI and LLM technologies, including fine-tuning, RAG, LLM evaluation, foundation model integration, advanced prompt engineering, and AI pipeline design.
  • Strong foundations in Machine Learning and Deep Learning, including practical experience with PyTorch and/or TensorFlow.
  • Solid understanding of statistics, linear algebra, optimization, recommendation systems, and ranking models.
  • Experience with cloud environments such as AWS or GCP and production MLOps pipelines using technologies such as MLflow, Kubeflow, or Airflow.
  • Knowledge of low-latency model serving, Docker, Kubernetes, observability, and monitoring of AI/ML systems in production.
  • Strong architectural skills, including the ability to evaluate technical trade-offs and design scalable AI systems.
  • Demonstrated ability to influence cross-functional teams without formal authority and communicate complex technical concepts clearly to non-technical stakeholders.
  • Proven experience mentoring and developing engineers.
  • Strong analytical thinking, autonomy, communication, collaboration, and strategic decision-making skills.
  • Experience applying AI to matching or recommendation systems, HRTech or B2B SaaS environments, open-source AI projects, or academic/industry publications and conferences is considered a plus.

  Benefits

  • Remote work model with flexibility in working hours.
  • Meal and food allowance through Caju.
  • Medical plan fully funded for the employee, with co-participation for eligible dependents.
  • Dental plan.
  • Life insurance.
  • Home office allowance.
  • Mental health support through benefits such as Zenklub.
  • Physical wellness support through benefits such as Gympass.
  • Six months of maternity leave.
  • Six months of paternity leave, also applicable to same-sex couples, non-birth mothers, and adoption cases.
  • Five days of marriage leave.
  • Internal recognition program with rewards redeemable through Caju.
  • Birthday day off.
  • Access to Creditas payroll-deducted credit, financial education, and marketplace benefits.
  • Annual Férias&Co credits to support vacation travel.
  • Access to coworking spaces through Woba.
  • Opportunity to become a shareholder.
  • Structured career development with technical and management paths through a career framework.
  • Opportunities for continuous learning, increased responsibility, and professional growth.