AI Engineering Manager

Job Responsibilities

• Own end-to-end engineering delivery of the real-time data serving infrastructure, including data serving layers, search indexes, and online feature delivery
• Drive engineering reliability and scalability of the real-time model serving infrastructure
• Lead engineering delivery of the agentic interface end-to-end
• Own LLM orchestration architecture for dialogue management, context handling, and session continuity
• Lead customer insight modeling pipeline and ensure high accuracy
• Lead machine learning pipeline engineering that surfaces insights about connected home usage patterns
• Manage sprint-cadence delivery across engineering teams with clear ownership, unblocking, and accountability
• Work closely with cross-functional teams across architecture, product, AI/ML ops, and the Video Intelligence team to manage feature and data dependencies
• Drive observability standards: APM span hierarchy, cost monitoring, escalation rate tracking, and alert thresholds
• Lead drift detection, confidence scoring pipeline monitoring, and production rollback readiness
• Prepare and deliver team health updates and milestone reviews to leadership
• Operate fluidly as a first-line or second-line manager depending on project needs — directly managing engineers when hands-on delivery leadership is required, and leading through senior engineers who manage their own teams in steadier execution phases
• Develop and grow Senior Engineers into technical leads who can carry day-to-day team ownership, while maintaining direct coaching relationships across the full team
• Build a high-trust, high-velocity team culture
• Comply with health and safety guidelines and rules; managers should also ensure compliance across their teams
• Protect Chamberlain Group's reputation by keeping information confidential
• Maintain professional and technical knowledge by attending educational workshops, reading professional publications, establishing personal networks, and participating in professional societies
• Contribute to the team effort by accomplishing related results and participating on projects as needed

Job Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
• 7+ years of software engineering experience, including 3+ years in an engineering leadership or management role
• Demonstrated ownership of real-time serving infrastructure and machine learning pipelines at production scale: low-latency APIs, feature stores, embedding indexes, model serving, or online scoring layers
• Experience building or leading LLM-powered or agentic systems: conversational AI, LLM orchestration, retrieval-augmented generation (RAG), or dialogue management
• Experience with ML behavioral modeling, anomaly detection, or time-series analysis
• Experience managing distributed engineering teams spanning geographies and employment models
• Proven track record delivering production APIs with strict SLA requirements (uptime and observability standards)
• Knowledge, Skills, and Abilities: Strong software engineering fundamentals: production-quality Python, system design, code review practices, and automated testing — this is not a configuration or no-code role
• Deep understanding of real-time serving architecture: API gateway patterns, vector search, and feature store read paths
• Working knowledge of LLM orchestration frameworks (LangChain or equivalent), retrieval-augmented generation (RAG) pipelines, prompt engineering, and AI agent workflow design
• Familiarity with ML anomaly detection techniques: behavioral baselines, scoring pipelines, false-positive management
• Experience with production observability tooling (Datadog APM or equivalent): span tracing, cost monitoring, alert threshold management
• Strong communication and stakeholder management skills — comfortable bridging distributed engineering execution with product and AI leadership
• Able to operate with autonomy in a fast-moving environment; capable of defining process where none yet exists

Preferred Job Requirements

• Master's degree in Computer Science, Computer Engineering, or related field
• AWS Certified Machine Learning Specialty or equivalent cloud ML certification
• Experience in IoT, smart home, or consumer device ecosystems where AI operates at or near the edge
• Background in occupancy modeling, event-sequence pipelines, or behavioral data for connected devices
• Prior experience building or scaling a real-time decisioning layer that interfaces between a data platform and user-facing product surfaces