[Remote] AI/ML Engineering Manager

Note: The job is a remote job and is open to candidates in USA. Caylent is a cloud native services company that helps organizations optimize their technology using AWS. They are seeking a Senior AI/ML Engineering Manager to lead and develop a team of ML engineers, manage customer engagements, and drive pre-sales efforts while ensuring technical quality and team growth.ResponsibilitiesHire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind. Build a team that raises the practice's overall standardDevelop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experiencedManage performance: Recognize strong contributors and address performance gaps directly and early. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve itStay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriatelyLead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagementShape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hardAdvise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act onDrive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliverLead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standardsOwn the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team. The expectation is the same in both cases — you are the person the engagement depends on technicallyRaise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice betterSkills10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacityDemonstrated people management experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructivelyDeep, 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 monitoringDeep 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 landscapeProven 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 onDeep 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 AWSProven 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 audiencesAWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – ProfessionalExperience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagementsExposure to varied industries and problem types in a consulting or client-facing contextDeep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deploymentsFluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customersBenefits100% remote workMedical Insurance for you and eligible dependentsGenerous holidays and flexible PTOCompetitive phantom equityPaid for exams and certificationsPeer bonus awardsState of the art laptop and toolsEquipment & Office StipendIndividual professional development planAnnual stipend for Learning and DevelopmentWork with an amazing worldwide team and in an incredible corporate cultureCompany OverviewAWS Premier Partner for Cloud Native Services It was founded in 2015, and is headquartered in Irvine, California, USA, with a workforce of 501-1000 employees. Its website is https://caylent.com.