AI Architect

We are seeking a visionary Senior AI Architect to design and build the intelligent orchestration layers and robust data architectures that power ADT’s next-generation AI initiatives.

In this role, you will be the driving force behind our enterprise adoption of state-of-the-art LLMs (Gemini Enterprise, OpenAI) and advanced AI orchestration frameworks.

Because powerful AI requires exceptional data foundations, you will focus heavily on designing the real-time data pipelines, relational and analytical engines, and retrieval systems necessary to ground our models in reality, leveraging streaming IoT, video, and sensor data.

Additionally, you will champion & partner with engineering teams on use of AI-native developer tools like Cursor and Claude Code to hyper-charge our SDLC.

Enterprise AI Strategy: Architect and deploy scalable AI solutions leveraging Gemini Enterprise, OpenAI, and Anthropic (Claude) models to solve complex business and security challenges.

Build Agentic Systems: Design and deploy multi-agent AI solutions with advanced orchestration, memory systems, and secure tool integration.

Data Architecture for AI: Design the underlying data architecture required to feed high-quality, real-time data into AI systems, emphasizing massively scalable relational and analytical data stores.

Real-Time AI Pipelines: Enable high-throughput processing of streaming IoT, video, sensor, and event data using event streaming and publish-subscribe messaging systems.

Multi-Modal AI Integration: Apply computer vision, event detection, anomaly detection, and video intelligence to real-world edge and cloud scenarios.

Developer Productivity: Spearhead the adoption of AI-native development environments, specifically driving the integration of Cursor and Claude Code, Gemini Enterprise alongside tools like Bitbucket & GitHub, into engineering workflows.

RAG & Context Systems: Architect scalable Retrieval-Augmented Generation (RAG) systems, integrating vector databases and semantic search to ground LLMs in enterprise data.

AI Platform Scale & Efficiency: Architect secure, scalable, and cost-efficient AI platforms across multi-cloud environments, optimizing model latency, token usage, and system costs.

Responsible AI & Governance: Implement AI governance, privacy preservation, security protocols, and compliance best practices.

Cross-Functional Leadership: Partner with Data Engineering, Product, and Security teams to mentor teams, guide architecture decisions, and ensure AI solutions are deeply integrated into ADT's ecosystem.

Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field (or an equivalent amount of work experience).

15+ years of core experience in software engineering, data engineering, or cloud architecture.

AI/ML Experience: 4+ years of hands-on experience designing and delivering production-grade machine learning or AI systems.

2+ years of direct experience building and deploying GenAI applications, LLMs, or agent-based solutions.

Platform & Integration Ecosystems: Hands-on experience working with GCP, and familiarity with Salesforce and Oracle Cloud platforms, including their corresponding data services and integration tools.

Enterprise AI Platforms: Experience with customer experience and service management AI platforms (such as Sierra, Google Agent Assist, or ServiceNow AI) is a strong plus.

System Design: Proven track record of designing and implementing complex, distributed solutions on multiple enterprise-scale platforms.

Core LLMs: Gemini Enterprise, OpenAI (GPT-4o), Anthropic (Claude).

Agent Frameworks: LangChain, LangGraph, AutoGen, CrewAI, or custom orchestration frameworks.

AI Developer Tools: Cursor, Claude Code, GitHub Copilot.

Data Pipelines & Event Streaming: Apache Kafka and Google Cloud Pub/Sub for real-time messaging, stream processing, and event-driven architectures.

Enterprise Data Stores: Google Cloud Spanner (for scalable, highly consistent relational data) and Google Cloud BigQuery (for large-scale data warehousing and analytical processing).

Context & Semantics: Vector Databases (BigQuery, Pinecone, pgvector, Milvus, Weaviate), embeddings, vector search, and semantic indexing.

Cloud & Infrastructure: GCP, Terraform, Vertex AI, Kubernetes, and modern microservice APIs.

Enterprise AI Platforms (Bonus): Sierra, Google Agent Assist, Gemini Enterprise, ServiceNow AI platforms.

Programming Languages: Strong programming skills in Python, with TypeScript, Java, or Go as a plus.

Certifications: Cloud or AI certifications (Google, Microsoft, AWS) are highly preferred.

Excellent communication, cross-functional collaboration, and creative problem-solving skills.