About the job
Exciting opportunity for a Senior AI Platform Engineer to architect and build scalable, production-grade AI platforms at Adobe. Leverage LLMs, generative models, and agentic systems to drive innovation in engineering productivity and AI adoption. Join a collaborative team shaping the future of intelligent platforms powering creativity across design, imaging, and personalization.
Responsibilities
Architect and evolve the AI platform powering Adobe Engineering — with a strong emphasis on Agentic AI systems and LLM-native architectures.
Design and implement scalable orchestration layers that coordinate LLMs, tools, APIs, memory stores, and multi-step reasoning workflows.
Build production-grade agent frameworks that support planning, task decomposition, tool invocation, multi-agent collaboration, and persistent memory.
Develop high-performance inference and runtime systems with strong guarantees around latency, reliability, observability, and cost efficiency.
Design evaluation and feedback systems that measure reasoning quality, task success, hallucination rates, and agent behavior — enabling rapid iteration and continuous improvement.
Integrate first-party and third-party foundation models into cohesive, adaptive systems using routing, model selection, guardrails, and fallback strategies.
Design data flows, session-level intelligence, and contextual memory systems that allow agents to operate coherently across interactions.
Partner with applied research, product, and platform teams to bring intelligent agentic capabilities into real developer-facing experiences.
Drive architectural strategy for AI for Engineering — connecting models, reasoning engines, tools, and data streams into adaptive AI systems.
Mentor senior engineers in modern AI system design, LLM orchestration patterns, and agent platform architecture.
Qualifications
Minimum
10+ years of experience building large-scale distributed systems, AI platforms, or intelligent service architectures.
Deep understanding of how LLMs behave in production environments — including prompting strategies, reasoning chains, tool usage, grounding techniques, hallucination mitigation, guardrails, and evaluation patterns.
Strong experience building AI-powered systems using LLM orchestration frameworks, model routing strategies, and multi-model pipelines.
Hands-on experience designing agentic systems — including reasoning loops, memory persistence, tool integration, state management, and multi-agent coordination.
Proven expertise in building scalable, cloud-native, microservices-based architectures with strong observability and reliability.
Experience designing evaluation systems for generative AI quality, task completion, and behavioral robustness.
Proficiency in TypeScript, Python and at least one systems language (Java, Go, C++), with experience building production AI services.
Strong systems thinking — ability to connect model capabilities, runtime constraints, and product requirements into coherent architectures.
Excellent multi-functional communication skills, with experience influencing architectural direction across research and engineering teams.
Preferred
Experience architecting AI assistants, copilots, or agent platforms in production environments.
Experience working with multimodal generative systems (text, image, video, motion).
Familiarity with tool-augmented LLM systems, RAG architectures, vector databases, and contextual memory systems.
Exposure to evaluation frameworks for generative AI quality and safety.
Experience contributing to open-source AI frameworks or publishing technical thought leadership.