Senior Director Platform and AI Engineering, Monitoring

Job Description

Summary:

The Sr. Director of Platform and AI Engineering, Monitoring, is a strategic leadership role responsible for defining and executing the product vision and roadmap for our AI-driven Monitoring platform and monitoring services. You will be at the forefront of integrating cutting-edge AI and M/L technologies to create new platform's capabilities, ensuring its reliability, scalability, and performance. You will lead a world-class team of engineers, fostering a culture of innovation and excellence within ADT. This leader will lead high performance team to conceptualize, develop, and launch innovative products and features that leverage AI to provide proactive, intelligent, and personalized security experiences for our customers. This role reports to the VP of Platform Engineering and will be a key member of the product and engineering leadership team.

The Senior Director of Platform & AI Engineering, Monitoring is a key leadership role responsible for architecting, scaling, and optimizing ADT's next-generation AI-powered monitoring platform. This leader will drive the convergence of AI/ML, real-time IoT orchestration, multimodal data fusion, and human-in-loop operations to deliver reliable, context-aware, and adaptive monitoring at scale.

Duties and Responsibilities:
Reporting to the VP of Platform Product and Engineering, this leader will oversee a team of engineers, ML specialists, and platform architects to design and build a resilient, scalable foundation for agentic monitoring - where automation and empathy work together seamlessly. The role spans end-to-end system engineering, AI Ops, AI inference optimization, and cloud-based alarm intelligence, ensuring ADT's monitoring services are both proactive and predictive.

1. Build and Evolve ADT's AI Monitoring Platform:

• Architect and lead the evolution of a cloud-native, microservices-based monitoring platform capable of ingesting billions of IoT events daily across sensors, cameras, and devices.
• Deliver a scalable AI orchestration layer that powers detection, triage, and adaptive response across home, vehicle, and enterprise environments.
• Lead design of a self-learning data fabric that unifies structured, unstructured, and streaming data to enable real-time decisioning.
• Drive infrastructure modernization - including serverless compute, event-driven architectures, and distributed data lakes for low-latency inference.
• Lead the Cross-Device Intelligence Strategy: Architect a platform capable of ingesting and synthesizing data from a wide array of IoT devices (beyond just cameras) to create a holistic and contextual understanding of the customer's environment, enabling more predictive and proactive monitoring services.

2. Deepen AI and ML Engineering Capabilities:

• Partner with Data Science leadership to productionize models for event classification, object detection, behavioral pattern recognition, and anomaly prediction.
• Build robust AI Ops pipelines to automate model deployment, monitoring, retraining, and governance.
• Integrate Generative AI (GenAI) to synthesize monitoring summaries, generate incident context, and power AI-assisted operator experiences.
• Implement reinforcement learning loops where models continuously improve through feedback from human agents and customer outcomes.

3. Advance Human-in-the-Loop Monitoring Systems:

• Design AI workflows that seamlessly integrate human expertise - balancing automation efficiency with empathy and precision.
• Develop AI-assisted incident management tools: event summarization, context retrieval, risk scoring, and next-best-action recommendations.
• Implement real-time feedback loops where operator inputs retrain and optimize AI models for greater situational understanding.
• Define and evolve adaptive escalation protocols that dynamically determine when to route events to human operators versus automated resolution.
• Personalized Monitoring Profiles: Develop AI-driven capabilities that allow customers to customize their monitoring preferences, enabling a tailored balance between automated AI responses and human engagement

4. Lead Cross-Device and Edge Intelligence:

• Drive engineering for edge-AI inference, enabling on-device decisioning for faster verification and lower bandwidth consumption.
• Orchestrate cross-device intelligence, unifying camera, audio, motion, and environmental data into a single contextual model.
• Build resilience into field-deployed systems with federated learning and failover monitoring across diverse connectivity environments.

5. Ensure Reliability, Observability, and Scale:

• Define platform-wide SLOs/SLIs for latency, uptime, and inference accuracy, ensuring mission-critical reliability.
• Implement modern observability frameworks across all AI and microservice layers.
• Build a culture of continuous deployment with zero-downtime upgrades, leveraging Kubernetes, Terraform, and CI/CD automation.
• Oversee multi-region, fault-tolerant infrastructure across public cloud environments (AWS, Google Cloud Platform, Azure).

6. Accelerate Innovation Through Simulation & Experimentation:

• Develop a digital twin and simulation environment for large-scale testing of alarm scenarios, sensor events, and agentic responses.
• Partner with Product and UX to rapidly prototype new monitoring experiences, testing desirability, feasibility, and performance.
• Establish internal "AI Experience Labs" to test multimodal AI applications - video, audio, voice, and textual incident data - in real environments.

7. Lead, Inspire, and Scale Engineering Talent:

• Build and mentor a diverse, high-performing team of platform, AI, and MLOps engineers.
• Cultivate a culture of technical excellence, experimentation, and measurable business impact.
• Drive alignment across Product, Data Science, and Operations to ensure AI technology translates into real-world value.

8. Monitoring video AI Technology:

• Intelligent Alert Triage & Prioritization: Design and implement AI models that intelligently triage and prioritize video events, distinguishing between critical and non-critical incidents to optimize human operator response times and efficiency.
• Human-in-the-Loop Verification: Develop workflows and interfaces that seamlessly integrate human operators for verification and contextual analysis of AI-generated alerts, ensuring accuracy and reducing false positives.
• Adaptive Response Protocols: Define and evolve adaptive response protocols where AI handles routine events autonomously, while escalating complex or ambiguous situations to human monitoring professionals for expert intervention.
• AI-Assisted Incident Management: Innovate on tools and features that empower human agents with AI-powered insights, such as summarized event timelines, relevant historical data, and predictive analytics, to enhance their decision-making during incidents.

Qualifications:

• 10+ years of engineering experience with at least 5+ years leading large, distributed teams delivering production-grade AI or platform systems.
• Proven expertise in cloud-scale architectures, real-time streaming, and microservices design.
• Deep understanding of AI/ML systems, including model lifecycle management, MLOps, feature stores, and data governance.
• Experience integrating GenAI, LLMs, or multimodal AI into customer-facing products.
• Preferred background in IoT, computer vision, or event-driven systems.
• Mastery of modern cloud infrastructure (AWS, Google Cloud Platform, or Azure) and container orchestration (Kubernetes, Docker).
• Strong analytical and problem-solving skills with a data-driven approach to decision-making.
• 10+ years of engineering experience with a track record of successfully launching and scaling B2B or B2C products.
• 5+ years of experience in a leadership role, managing and mentoring a team of technical leaders
• Deep understanding of containerization and orchestration technologies (Kubernetes, Docker), infrastructure as code (Terraform, CloudFormation), and CI/CD principles and tools.
• Proven ability to develop and execute a product strategy that drives business growth.
• Excellent communication, presentation, and interpersonal skills, with the ability to influence and align stakeholders at all levels.
• Strong analytical and problem-solving skills, with a data-driven approach to decision-making.
• Experience in the security, smart home, or IoT industries is a plus.
• Bachelor's or master's degree in computer science, AI, or related field (PhD preferred).

Personal Characteristics:

• AI-Native Thinker: Passionate about creating AI systems that augment human intelligence.
• Operational Builder: Scales robust, real-time systems that never fail under pressure.
• Collaborative Leader: Inspires teams across product, data, and engineering to execute at startup speed and enterprise scale.
• Customer-Obsessed: Designs with empathy, safety, and trust as non-negotiables.
• Innovative and Analytical: Combines creative prototyping with rigorous experimentation.
• Results-Driven: Balances long-term platform vision with measurable short-term impact.

Background checks will be conducted during the employment process. Any information will be reviewed through an individualized assessment in accordance with the Philadelphia Fair Criminal Record Screening Standards Ordinance.