AI/ML Engineering Manager

Caylent

1d•Remote

About The Position

Caylent is a cloud-native services company that helps organizations leverage Amazon Web Services (AWS). This is a senior role for an individual who is both deeply technical and accountable for the growth and performance of a team of ML engineers and architects. The role reports to the Director of AI/ML and involves owning hiring, development, team health, leading complex customer engagements, shaping architecture, and driving pre-sales. The ideal candidate thrives on the combination of technical leadership and team management.

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 including hiring, performance calibration, career development, and conducting difficult conversations 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 (data engineering, feature engineering, training, deployment, monitoring).
Deep expertise in at least two or three ML domains (e.g., classical ML, computer vision, NLP, time series) and the judgment to advise across the broader ML landscape.
Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and AI operations complexity into actionable standards.
Deep expertise across foundation model adaptation (fine-tuning, alignment, inference optimization, distributed training) combined with RAG and agentic system design (multi-agent architectures, MCP integration, human-in-the-loop patterns on AWS).
Proven ability to operate independently in complex, ambiguous customer environments, navigating priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.

Nice To Haves

AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
Exposure to varied industries and problem types in a consulting or client-facing context.
Deep fluency in responsible AI practices (model evaluation, bias detection, fairness frameworks, AI governance) applied in enterprise deployments.
Fluency in AIOps patterns (agentic workflows for anomaly detection, automated root cause analysis, remediation) and translating AI operations outcomes into measurable business value.

Responsibilities

Hire and build a team of ML engineers and architects, setting the technical bar and overseeing technical assessments.
Develop people through regular 1:1s, providing feedback, and investing in career growth.
Manage performance by recognizing strong contributors and addressing performance gaps.
Stay close to team utilization and staffing, informing the staffing team of skills evolution and preferences.
Lead ML assessments for customers, evaluating infrastructure, data pipelines, model lifecycle, and organizational readiness.
Shape architecture on engagements, serving as the senior technical authority and ensuring technical quality.
Advise on ML operations, helping customers build sustainable ML systems and translating MLOps/LLMOps complexity into actionable standards.
Drive pre-sales efforts by partnering with sales and solutions teams, providing technical depth for accurate scoping and building prospect confidence.
Lead engagements end-to-end, driving architecture and solution design from kickoff through delivery.
Own the technical relationship with clients, serving as the primary contact or senior technical authority.
Mentor engineers and architects, contribute to technical interviews, and build reference architectures and accelerators.
Contribute to the growth of the broader ML practice.