AI/ML Engineering Manager leading ML teams and complex customer engagements at Caylent, an AI-first AWS cloud services company. Shaping production ML architecture, MLOps standards, and pre-sales strategy.
Responsibilities
Hire and build the ML team; set technical standards, oversee technical assessments, and make hiring decisions.
Develop team members through structured 1:1s, feedback, and career growth investment.
Manage performance, partner with HRBPs and the Director of AI/ML, and advocate for the team.
Monitor staffing and ensure team members are placed in appropriately challenging engagements.
Lead end-to-end ML assessments of customer environments and produce executive recommendations.
Serve as senior technical authority, shape architecture, and ensure technical quality on engagements.
Advise customers on sustainable MLOps, LLMOps, production monitoring, and ML operations standards.
Drive pre-sales with sales and solutions teams during scoping and proposal phases.
Lead engagement architecture and solution design from kickoff through delivery.
Own architect-level client outcomes or provide senior technical oversight across the team.
Mentor engineers and architects, contribute to technical interviews, and build reference architectures and reusable ML accelerators.
Report to the Director of AI/ML.
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 the country where the position is located; Caylent cannot sponsor visa or work permit applications.
AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional are strong differentiators.
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 and AIOps patterns is a strong differentiator.
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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