AI/ML Engineering Manager

• Hire and build the ML team, set technical standards, oversee technical assessments, and make hiring decisions.
• Conduct structured 1:1s, provide feedback, and support team members’ career growth.
• Manage performance, partner with HRBPs and the Director of AI/ML, and advocate for the team.
• Monitor staffing and ensure team members are appropriately placed across engagements.
• Lead end-to-end ML assessments of customer environments and produce executive recommendations.
• Shape engagement architecture and serve as senior technical authority.
• Advise customers on MLOps, LLMOps, production monitoring, and sustainable ML systems.
• Partner with sales and solutions teams on scoping and proposals.
• Lead architecture and solution design from kickoff through delivery.
• Own architect-level client outcomes or provide senior technical oversight.
• Mentor engineers and architects, contribute to technical interviews, and develop reference architectures and reusable ML accelerators.

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Demonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively.
• Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
• Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
• Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on.
• Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS.
• Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.
• Current legal authorization to work in Brazil; Caylent cannot sponsor visa or work permit applications.
• Up to 25% travel may be required depending on business needs.

• 100% remote work
• Flexible Time Off
• Competitive phantom equity
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Individual professional development plan
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture