AINative Platform Engineer – Media Analytics

The Opportunity

Adobe Media Analytics powers some of the worlds largest digital experiences from major live events to alwayson streaming platforms. Our systemsoperateata massivescale with strict requirements around latency reliability cost efficiency and correctness.

Media is amature critically important product. It doesnt tolerate experimentation in production which makes building AIassisted analytics and workflows managed by agents especially challenging and especially interesting.

We seek an AINative Platform Engineer to help evolve the data and service foundations behind Adobe Analytics and Customer Journey Analytics.

You will ensure systems remain accurate and available during heavy traffic spikes. You will also develop new AI-powered experiences including assisted insights conversational analytics and workflows directed by users.

If you enjoy building production systems that matter and believe AI should be applied responsibly observably and with strong engineering rigor youllfeel at home here.

WhatYoullDo

• Build and operate cloudnative distributed information and application platforms that power analytics and AIassisted features at scale.

• Design and evolveeventdriven and streaming architectures(Kafkabased and similar) that remain reliable under peak traffic and failure conditions.

• Translate product and business requirements intorobust productionready systems not prototypes.

• Improve existing architectures byidentifyingoperational risks performance bottlenecks and cost inefficiencies.

• Contribute across the full lifecycle: design implementation code review testing deployment monitoring and oncall.

• Participate in an oncall rotation and help improve incident response observability and system resilience.

• Enable AIassisted experiences by incorporating AI/ML functionalities on top of strong data and platform foundations.

• Mentor other engineers and help raise the technical bar across the team.

Whatyoull need to succeed

This isnota pure AI role andnota traditional bigdata role.
Were looking for engineers who understand thatAI systems are only as good as the platforms they run on.

Youlikely have:

• 7 years of handson experience building andoperatingdistributed systems backend services or largescale data platforms.

• Strong experience withevent streaming and messaging systems(Kafka Kafka Streams Flink Kinesis or similar).

• Experience buildingdata pipelinesusing Spark Databricks Flink or comparable technologies.

• Solid understanding ofoperating production systems: Kubernetes CI/CD monitoring alerting and incident response.

• Experience designing andoperatingscalable REST services.

• Strong engineering fundamentals (Java preferred; Python familiaritya plus).

• A pragmatic approach: you care about correctness performance reliability and cost not just features.

AIRelated Expectations (Important)

• Be comfortable integrating AI/ML capabilities into production systems.

• Think critically aboutobservability evaluation and failure modesfor AIassisted features.

• Care aboutresponsible AI principles(privacy transparency safety) when shipping real userfacing functionality.

Nice to Have

• Scala experience.

• Strong observability background (Prometheus Grafana SplunkFluentbit).

• Experience operating systems during hightraffic or eventdriven spikes.

• Priorexposure to AIassisted features LLM APIs or AIpowered services in production.

• Familiarity with monitoring AI/ML systems (telemetry drift performance signals).