Principal Scientist - Adobe Brand Intelligence

Adobe Adobe · Enterprise · Bangalore, India

Principal Scientist role focused on software architecture for Adobe Brand Intelligence, a platform that uses AI agents and enterprise context to enhance on-brand content creation and customer experiences. The role involves building and scaling distributed software platforms, defining technical vision, and operationalizing AI models into production-ready systems. Key responsibilities include architecting backend services, APIs, and platform infrastructure, integrating AI capabilities, and collaborating with ML teams. Experience with LLM application architectures, RAG, and agentic systems is preferred.

What you'd actually do

  1. Define the long-term software architecture and technical vision for ABI.
  2. Build the foundational platform delivering AI capabilities consistently across Adobe applications, web experiences, and enterprise ecosystems.
  3. Drive architectural decisions for backend services, web applications, APIs, and platform infrastructure.
  4. Partner with the ML team to operationalize AI models into scalable, secure, enterprise-ready software systems.
  5. Direct engineering strategy across multiple teams while offering mentorship to senior engineers and technical leaders.

Skills

Required

  • 12+ years of experience building large-scale distributed software systems.
  • Proven background in developing extensive software systems supporting millions of users or sizable enterprise clients.
  • Significant experience in backend development using Java, Python, or comparable technologies, along with a solid grasp of distributed systems and cloud-native platforms.
  • Experience building modern web applications using frontend frameworks such as React, TypeScript, or similar technologies.
  • Proven experience building extensible APIs, SDKs, plug-in architectures, and integration frameworks.
  • Experience integrating software platforms into desktop applications, web applications, SaaS platforms, or enterprise ecosystems.
  • Solid knowledge of software architecture patterns, platform engineering, scalability, resiliency, observability, security, and operational excellence.
  • Experience working closely with Machine Learning and Applied AI teams to incorporate AI features into enterprise software products.
  • Demonstrated skill in guiding technical direction across multiple engineering teams.
  • Excellent communication skills to explain complex architectural concepts to engineers, product leaders, and executives.

Nice to have

  • Experience developing AI-native enterprise software platforms.
  • Experience with LLM application architectures, RAG, AI orchestration frameworks, MCP, agentic systems, or intelligent workflow platforms.
  • Experience building integrations for Adobe Creative Cloud applications, Adobe Experience Cloud, or related creative ecosystems.
  • Experience building browser extensions, desktop application plug-ins, SDKs, or extensibility platforms.
  • Familiarity with enterprise identity, security, governance, and compliance frameworks.
  • Contributions to open-source software or widely adopted developer platforms.
  • Experience leading architecture across globally distributed engineering organizations.

What the JD emphasized

  • lead the software architecture
  • Build the foundational platform
  • operationalize AI models
  • enterprise-ready software systems
  • Direct engineering strategy
  • Define the comprehensive architecture
  • highly scalable distributed systems
  • Integrate AI capabilities into production
  • enterprise-level APIs
  • platform architecture
  • engineering standards
  • contemporary software engineering methods
  • AI-native enterprise software platforms
  • LLM application architectures
  • AI orchestration frameworks
  • agentic systems
  • intelligent workflow platforms

Other signals

  • Partner with the ML team to operationalize AI models into scalable, secure, enterprise-ready software systems.
  • Integrate AI capabilities into production through effective orchestration and enterprise-level APIs.
  • Experience working closely with Machine Learning and Applied AI teams to incorporate AI features into enterprise software products.
  • Experience developing AI-native enterprise software platforms.
  • Experience with LLM application architectures, RAG, AI orchestration frameworks, MCP, agentic systems, or intelligent workflow platforms.