Chief Architect for Adobe Intelligence Platform

Adobe; San Jose (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Flexible work hours

Health insurance

Employee stock purchase plan

Job summary

A leading tech firm in San Jose seeks a Sr. Principal Architect for the Adobe Intelligence Platform. In this role, you'll lead a team in designing and evolving AI-native architecture to enhance interoperability across products. You'll ensure system resilience, guide technical decisions, and mentor engineers. With over 15 years in software engineering and strong expertise in AI architectures, you’ll be pivotal in shaping a unified ecosystem that drives agentic experiences across Adobe offerings.

Qualifications

• 15+ years of software engineering experience in platform-scale systems.
• Deep expertise in modern AI architectures like LLM serving and retrieval-augmented generation.
• Experience designing large-scale distributed systems with strong fundamentals in consensus.

Responsibilities

• Lead architecture for Adobe Intelligence Platform covering agentic orchestration.
• Design scalable registries and infrastructures for AI applications.
• Establish standards for fault tolerance and chaos engineering.

Skills

AI architecture

System design

Resilience engineering

Team leadership

Cross-product integration

Education

BS/MS/PhD in Computer Science or related field

Tools

Rust

Go

Python

TypeScript

Job description

About Adobe Intelligence Platform

Adobe Intelligence Platform is the foundational AI infrastructure that powers agentic experiences across the Adobe product portfolio. The platform enables interoperability of agents, capabilities, and workflows across the Adobe Creative Cloud, Experience Cloud, Document Clouds, and future product lines. It is the connective tissue that transforms Adobe from a suite of best-in-class tools into a unified, AI-native ecosystem.

This platform encompasses agentic orchestration, model gateway infrastructure, knowledge graph services, semantic memory systems, MCP registries, skill and capability catalogs, and the planning and reasoning layers that enable autonomous and semi-autonomous workflows at enterprise scale.

Opportunity

We are seeking a Sr. Principal Architect to serve as the technical leader and chief decision-maker for the Adobe Intelligence Platform engineering team. You will lead a team of architects and senior engineers responsible for designing, building, and evolving the platform that unlocks AI-native interoperability across every Adobe product.

This is not a strategy-only role. You will be hands‑on in defining system boundaries, authoring architecture decision records, reviewing critical code paths, and setting the technical bar for the entire organization. You will own the long-range technical vision while driving execution on near-term deliverables with a bias toward production-quality, resilient systems.

You will report to the VP of AI Platform Engineering and work closely with product leadership, applied research, and engineering teams across all Adobe business units.

What You Will Do

• Platform Architecture Leadership: Own the end-to-end technical architecture for Adobe Intelligence Platform, including agentic orchestration, model gateways, MCP registries, knowledge graph infrastructure, semantic memory, skill catalogs, and planning and reasoning layers.
• Agentic Systems Design: Architect multi-agent harnesses that orchestrate agent lifecycle management, inter-agent communication protocols, capability discovery, delegation, and compositional planning across heterogeneous agent runtimes.
• Agent, MCP and Skills Registries: Design and scale a common Adobe registry for agents, MCP (Model Context Protocol) client/servers, skill and capability catalogs, and gateway infrastructure that enables secure, discoverable, and composable tool and service integrations across product boundaries.
• Knowledge Graph & Memory Systems: Lead the design of knowledge graph infrastructure and agentic memory systems (episodic, semantic, procedural) that enable agents to reason over structured and unstructured knowledge with low-latency retrieval and contextual grounding.
• LLM Infrastructure & Model Gateway: Architect the model abstraction, routing, and gateway layer that supports multi-model orchestration with circuit breakers, progressive rollouts, automated rollback, and cost-aware routing across proprietary and open-weight models.
• Safety, Governance and Trust: Partner closely with Security, Privacy and Legal colleagues to ensure the platform has observability, security, and enterprise governance controls around safety and trust built into the end-to-end architecture.
• Resilience & Production Excellence: Establish platform-wide standards for fault tolerance, observability, chaos engineering, progressive deployments, and automated recovery. Ensure every component is designed with the assumption that any dependency can fail at any time.
• Technical Decision Making: Serve as the final technical authority on architecture decisions, technology selection, and build‑vs‑buy evaluations. Author and maintain Technical Decision Documents (TDDs) and ensure decisions are well-documented, reversible where possible, and grounded in measurable criteria.
• Team Leadership & Mentorship: Lead and mentor a global team of architects and principal engineers. Establish architectural review processes, technical standards, and a culture of rigorous engineering that values quality, security, and maintainability.
• Cross-Product Integration: Partner with engineering leadership across Creative Cloud, Experience Cloud, and Document Cloud to ensure seamless integration of AI platform capabilities into existing and future product workflows.
• Security & Trust Architecture: Champion security-by-design principles including zero-trust networking, post-quantum cryptographic readiness, supply chain integrity, and data governance across all platform services.

What You Will Bring

• 15+ years of software engineering experience with at least 3 years in a principal+ or distinguished architect role leading platform-scale systems.
• Deep expertise in modern AI architectures including LLM serving, retrieval-augmented generation (RAG), agentic orchestration, multi-model routing, and gateway design.
• Demonstrated experience designing and operating large-scale distributed systems with strong fundamentals in consensus, partitioning, consistency models, and failure domain isolation.
• Expert-level knowledge of agentic AI patterns including planning and reasoning engines, tool use and function calling, skills, rules, agentic memory architectures, capability discovery, and multi-step workflow orchestration.
• Strong background in resilient system design including circuit breakers, bulkheads, backpressure mechanisms, progressive deployments, automated rollbacks, and chaos engineering practices.
• Production experience with modern systems languages (Rust, Go, Python, TypeScript) and a strong preference for type-safe, memory-safe toolchains.
• Experience designing and scaling API gateway and service mesh architectures with sophisticated routing, rate limiting, and observability.
• Track record of leading and mentoring teams of senior architects and engineers in fast-moving, high-stakes environments.
• BS/MS/PhD in Computer Science, Distributed Systems, or a related field, or equivalent demonstrated expertise.

What Sets You Apart

• Direct experience building or leading MCP (Model Context Protocol) infrastructure, registries, or gateway systems at scale.
• Contributions to open-source AI infrastructure projects or published research in agentic systems, knowledge representation, or distributed AI architectures.
• Experience operating AI platforms that serve hundreds of millions of end users across multiple product lines.
• Hands‑on experience with knowledge graph technologies (RDF/OWL, property graphs, vector stores, hybrid retrieval) and their application to enterprise-scale semantic search and contextual grounding.
• Expertise in streaming data architectures (Kafka, Flink, Pulsar) and event-driven system design for real-time agentic workflows.
• Deep understanding of AI safety, alignment, and responsible AI frameworks as they apply to autonomous agent systems deployed at enterprise scale.

Equal Employment Opportunity

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