AI/ML Engineering Manager

Job Summary

Required Qualifications

• 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
• Strong differentiators
• 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, and AI governance — applied in enterprise deployments
• Fluency 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 customers

Additional Requirements