AI/ML Engineering Manager

This is a fully remote position, open to applicants in Mexico.

📋 Description

• Lead and develop a team of machine learning engineers and architects.

• Recruit team members, establish technical standards, supervise technical assessments, and make hiring decisions.

• Conduct structured one-on-ones, provide constructive feedback, and invest in the professional growth of team members.

• Manage performance, acknowledge high contributors, address performance issues, and collaborate with HR business partners and the Director of AI/ML when necessary.

• Oversee staffing and utilization across projects and communicate skill progression and preferences to the staffing team.

• Conduct comprehensive ML assessments of customer environments and deliver recommendations for executive decisions and future engagements.

• Act as the senior technical authority on customer projects, shape architecture, ensure technical quality, and make critical trade-off decisions.

• Provide guidance to customers on MLOps, LLMOps, production monitoring, and sustainable ML systems.

• Collaborate with sales and solutions teams on project scoping and proposals to assist pre-sales efforts.

• Drive architecture and solution design from project kickoff to delivery, remove obstacles for teams, and uphold Caylent's quality standards.

• Own architect-level client outcomes or provide senior technical oversight across engagement teams.

• Mentor engineers and architects, participate in technical interviews, and develop reference architectures and reusable ML accelerators.

• Report directly to the Director of AI/ML.

• This role may involve up to 25% travel based on business requirements.

⛳️ Requirements

• Over 10 years of experience in machine learning or AI, with a proven history of leading client-facing engagements in a consulting or advisory role.

• Demonstrated experience in people management, including hiring, performance calibration, career development, and the ability to engage in difficult conversations directly and constructively.

• Extensive, up-to-date knowledge of the AWS ML and GenAI ecosystem, with the capability to make and substantiate architectural decisions throughout the complete ML lifecycle—from data and feature engineering to training, deployment, and monitoring.

• Profound expertise in at least two or three ML domains—such as classical ML, computer vision, NLP, time series, or others—paired with the judgment to evaluate, architect, and advise across the broader ML landscape.

• Proven ability to design and manage production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexities into standards that engineering teams can implement and executives can act upon.

• Extensive knowledge in foundation model adaptation—including 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.

• Demonstrated capacity to operate autonomously in complex, ambiguous customer environments—managing competing priorities, aligning stakeholders, and interpreting ML trade-offs into business risk and value for both technical and executive audiences.

• Current legal authorization to work in Mexico; Caylent is unable to sponsor visa or work permit applications.

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional are strong differentiators, though not mandatory.

• Experience in shaping practice-level standards, reference architectures, and reusable ML accelerators across various engagements is a significant differentiator.

• Exposure to diverse industries and problem types in a consulting or client-facing role is a notable differentiator.

• Strong fluency in responsible AI practices—model evaluation, bias detection, fairness frameworks, and AI governance—is a distinguished asset.

• Proficiency in AIOps patterns—designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms—is a valuable differentiator.

• Expected to advise across a wide technical stack, including AWS ML services, multi-provider LLM APIs, data platforms, vector databases, ML frameworks, MLOps and governance tools, LLM evaluation and safety tools, and inference optimization technologies.

🏝️ Benefits

• 100% remote work.

• Medical insurance for you and eligible dependents.

• Generous holidays and flexible PTO.

• Competitive phantom equity.

• Coverage 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.

• Opportunity to work with an amazing global team and within an incredible corporate culture.