Summary:
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.
Duties and Responsibilities:
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Enterprise AI Strategy: Architect and deploy scalable AI solutions leveraging Gemini Enterprise, OpenAI, and Anthropic (Claude) models to solve complex business and security challenges.
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Build Agentic Systems: Design and deploy multi-agent AI solutions with advanced orchestration, memory systems, and secure tool integration.
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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.
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Real-Time AI Pipelines: Enable high-throughput processing of streaming IoT, video, sensor, and event data using event streaming and publish-subscribe messaging systems.
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Multi-Modal AI Integration: Apply computer vision, event detection, anomaly detection, and video intelligence to real-world edge and cloud scenarios.
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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.
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RAG & Context Systems: Architect scalable Retrieval-Augmented Generation (RAG) systems, integrating vector databases and semantic search to ground LLMs in enterprise data.
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AI Platform Scale & Efficiency: Architect secure, scalable, and cost-efficient AI platforms across multi-cloud environments, optimizing model latency, token usage, and system costs.
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Responsible AI & Governance: Implement AI governance, privacy preservation, security protocols, and compliance best practices.
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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.
Qualifications and Requirements:
Education:
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Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field (or an equivalent amount of work experience).
Experience:
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15+ years of core experience in software engineering, data engineering, or cloud architecture.
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AI/ML Experience: 4+ years of hands-on experience designing and delivering production-grade machine learning or AI systems.
GenAI Experience:
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2+ years of direct experience building and deploying GenAI applications, LLMs, or agent-based solutions.
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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.
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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.
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System Design: Proven track record of designing and implementing complex, distributed solutions on multiple enterprise-scale platforms.
Technical Expertise:
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Core LLMs: Gemini Enterprise, OpenAI (GPT-4o), Anthropic (Claude).
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Agent Frameworks: LangChain, LangGraph, AutoGen, CrewAI, or custom orchestration frameworks.
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AI Developer Tools: Cursor, Claude Code, GitHub Copilot.
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Data Pipelines & Event Streaming: Apache Kafka and Google Cloud Pub/Sub for real-time messaging, stream processing, and event-driven architectures.
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Enterprise Data Stores: Google Cloud Spanner (for scalable, highly consistent relational data) and Google Cloud BigQuery (for large-scale data warehousing and analytical processing).
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Context & Semantics: Vector Databases (BigQuery, Pinecone, pgvector, Milvus, Weaviate), embeddings, vector search, and semantic indexing.
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Cloud & Infrastructure: GCP, Terraform, Vertex AI, Kubernetes, and modern microservice APIs.
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Enterprise AI Platforms (Bonus): Sierra, Google Agent Assist, Gemini Enterprise, ServiceNow AI platforms.
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Programming Languages: Strong programming skills in Python, with TypeScript, Java, or Go as a plus.
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Certifications: Cloud or AI certifications (Google, Microsoft, AWS) are highly preferred.
Professional Skills:
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Excellent communication, cross-functional collaboration, and creative problem-solving skills.