AI Engineering Manager

Chamberlain Group•Oak Brook, IL

1d•$102,600 - $193,425•Remote

About The Position

Chamberlain Group (CG) is a global leader in intelligent access and Blackstone portfolio company. Powered by our myQ technology, we make access simple and secure for millions of homeowners, businesses, and communities worldwide. Our flagship brands, LiftMaster® and Chamberlain® , are found in 51+ million homes, and 14 million+ people rely on the myQ® app daily. This role is within Chamberlain Group's Engineering Function. This manager leads engineering teams focused on building and delivering AI capabilities that power intelligent, connected home experiences for our customers. This is a US-based leadership role managing a distributed engineering team. Engineering teams in this role are responsible for the real-time data serving infrastructure that powers personalized, low-latency experiences; the agentic interface and LLM orchestration layer that enables intelligent interactions with customers; and the machine learning models that detect anomalies and surface insights to keep customers informed and in control of their connected home. This role will work with cross-functional teams across architecture, product, AI/ML ops, and the Video Intelligence team. Success in this role is to deliver and maintain the platform as a unified source of truth to deliver intelligent, personalized experiences to customers. This is a senior software engineering leadership role — strong programming fundamentals and hands-on fluency with real-time serving, LLM systems, and ML pipelines are required.

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).
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

Nice To Haves

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
Familiarity with OpenSearch, pgvector, or equivalent embedding index infrastructure
Understanding of A/B experimentation infrastructure and feature flag-driven rollout strategies
Experience with ArgoCD, CI/CD pipelines, and gradual traffic ramp strategies for ML model deployments

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
Own 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
Lead hiring and onboarding of engineers
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