AI/ML Engineering Manager
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Job Description
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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