AI Architect

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:

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.

Qualifications and Requirements:

Education:

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

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.

GenAI Experience:

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.

Technical Expertise:

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.

Professional Skills:

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