AI/ML Engineering Manager
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Job Description
AI/ML Engineering Manager leading ML engineers and architects at Caylent, an AI-first AWS cloud services company. Shaping production ML architecture, customer engagements, pre-sales, and practice growth.
Responsibilities:
- Lead and grow a team of ML engineers and architects
- Hire team members, set technical standards, oversee technical assessments, and make hiring decisions
- Conduct structured 1:1s, provide feedback, and invest in team members' career growth
- Manage performance, recognize strong contributors, address performance gaps, and partner with HRBPs and the Director of AI/ML when needed
- Monitor staffing and utilization across engagements and communicate skills evolution and preferences to the staffing team
- Lead end-to-end ML assessments of customer environments and produce recommendations for executive decisions and future engagements
- Serve as senior technical authority on customer engagements, shape architecture, ensure technical quality, and make key tradeoff decisions
- Advise customers on MLOps, LLMOps, production monitoring, and sustainable ML systems
- Partner with sales and solutions teams on scoping and proposals to support pre-sales
- Drive architecture and solution design from kickoff through delivery, unblock teams, and ensure Caylent quality standards
- Own architect-level client outcomes or provide senior technical oversight across engagement teams
- Mentor engineers and architects, contribute to technical interviews, and build reference architectures and reusable ML accelerators
- Report to the Director of AI/ML
- Role may require up to 25% travel depending on business needs
Requirements:
- 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 Mexico; Caylent is not able to sponsor visa or work permit applications
- AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional are strong differentiators, not requirements
- Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements is a strong differentiator
- Exposure to varied industries and problem types in a consulting or client-facing context is a strong differentiator
- Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — is a strong differentiator
- Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — is a strong differentiator
- Expected to prescribe across a broad 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
- 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
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