Senior AI Platform Engineer- Data and Systems
AI summary of the role
This is a systems-first engineering role on the Adobe Express Data Platform team, building the foundational infrastructure that powers AI, analytics, and autonomous agents at scale.
No longer in the current catalog. Last included 2026-09-09. Check the employer’s posting for availability.
What you’ll do
- Design and build streaming-first data pipelines that collapse end-to-end latency from hours to minutes through event-driven architectures.
- Own and extend the ML Attribute Store with low-latency online serving and unified batch/streaming aggregation to prevent training-serving skew.
- Build MCP-compatible Agent Data APIs and tool servers for autonomous AI agents to discover and query the lakehouse.
- Develop agentic frameworks for automated anomaly detection, pipeline self-healing, and root cause analysis.
What you’ll bring
- 6+ years of experience in data platform engineering, distributed systems, or backend infrastructure at scale.
- Deep hands-on experience with Apache Spark, Databricks, Delta Lake, or equivalent lakehouse technologies.
- Proven track record building and operating large-scale pipelines processing billions of events daily with sub-hour latency SLAs.
- Strong experience with streaming systems: Kafka, Kinesis, Flink, Spark Structured Streaming, or Delta Live Tables.
Technologies
Apache Spark · Databricks · Delta Lake · Kafka · Kinesis · Flink · LangChain · LangGraph · MCP · Kubernetes
About Adobe
Builds creative, document, and enterprise customer-experience software, now embedding Firefly AI and agents across creator and marketer workflows.
Public · 5000+ people
Source and classification
Internal deployment & tooling · Evidence for this classification:
The Opportunity Adobe Express Data Platform is the intelligence backbone for millions of creators- a billion-event-per-day system spanning streaming, feature serving, agent data APIs, and a lakehouse that powers every personalization decision, experiment, and AI workflow. We are evolving it into a streaming-first, self-healing, agent-ready Lakehouse and we need engineers who challenge the status quo, move fast, and default to an agentic-first approach for every problem they encounter. This is a systems-first engineering role. You won’t build ML models, you’ll build the foundational infrastructure that makes AI, analytics, and autonomous agents possible at scale. You’ll bring the conviction that any manual, repetitive, or slow platform workflow is a candidate for agentic automation and the engineering skill to make that real. We are tackling hard, consequential problems: collapsing
More from the job description
The Opportunity Adobe Express Data Platform is the intelligence backbone for millions of creators- a billion-event-per-day system spanning streaming, feature serving, agent data APIs, and a lakehouse that powers every personalization decision, experiment, and AI workflow. We are evolving it into a streaming-first, self-healing, agent-ready Lakehouse and we need engineers who challenge the status quo, move fast, and default to an agentic-first approach for every problem they encounter. This is a systems-first engineering role. You won’t build ML models, you’ll build the foundational infrastructure that makes AI, analytics, and autonomous agents possible at scale. You’ll bring the conviction that any manual, repetitive, or slow platform workflow is a candidate for agentic automation and the engineering skill to make that real. We are tackling hard, consequential problems: collapsing multi-hour pipeline latency to real-time, building MCP-compatible agent data APIs so autonomous AI systems can query and reason over platform data, evolving our ML Attribute Store with low-latency online feature serving, and pioneering AI-powered data governance that replaces manual operational toil with self-healing pipelines. Our team’s motto is simple: make the platform simpler, faster, and more reliable. Shipping fast isn’t reckless here - it’s a discipline. What You’ll Do Design and build st [... source excerpt omitted ...] va or Go is a plus. Experience with cloud platforms (AWS or Azure), containerization (Docker, Kubernetes), and CI/CD for data pipelines. AI-Native Engineering & Agentic Systems Production experience integrating LLMs into engineering workflows — not prototypes, but systems running against real data with real users. Includes prompt engineering, tool-use/function-calling, structured output parsing, and context window management. Hands-on experience with agentic AI frameworks and multi-agent orchestration (LangChain, LangGraph, CrewAI, AutoGen, or custom agent loops with memory, planning, and tool routing). Understanding of MCP (Model Context Protocol) and/or A2A protocols for exp [... source excerpt omitted ...] o as technical debt. Extreme bias for action and time-to-market: you ship iteratively, prefer “good enough now” over “perfect later,” and unblock yourself. You measure success in production impact, not design docs. Systems thinker who traces dependencies, considers second-order effects, and asks “why did this break?” not just “how do I fix it?” End-to-end ownership from design through production through 2 AM incident response. Platform reliability is personal. Preferred Qualifications Experience building AI-powered developer tools, self-serve data platforms, or code generation agents that reduce engineering toil. Experience migrating batch-first data architectures to streamin
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