Ai‑native Platform Engineer – Media Analytics

Adobe Adobe · Enterprise · Bucharest, Romania

This role focuses on building and operating cloud-native, distributed platforms for Adobe's Media Analytics products, incorporating AI/ML functionalities to enable AI-assisted experiences. The emphasis is on robust, production-ready systems with strong engineering rigor, reliability, and cost efficiency, rather than pure AI research or prototyping.

What you'd actually do

  1. Build and operate cloud‑native, distributed information and application platforms that power analytics and AI‑assisted features at scale.
  2. Design and evolve event‑driven and streaming architectures (Kafka‑based and similar) that remain reliable under peak traffic and failure conditions.
  3. Translate product and business requirements into robust, production‑ready systems, not prototypes.
  4. Improve existing architectures by identifying operational risks, performance bottlenecks, and cost inefficiencies.
  5. Enable AI‑assisted experiences by incorporating AI/ML functionalities on top of strong data and platform foundations.

Skills

Required

  • 7+ years of hands‑on experience building and operating distributed systems, backend services, or large‑scale data platforms.
  • Strong experience with event streaming and messaging systems (Kafka, Kafka Streams, Flink, Kinesis, or similar).
  • Experience building data pipelines using Spark, Databricks, Flink, or comparable technologies.
  • Solid understanding of operating production systems: Kubernetes, CI/CD, monitoring, alerting, and incident response.
  • Experience designing and operating scalable REST services.
  • Strong engineering fundamentals (Java preferred; Python familiarity a plus).
  • Pragmatic approach: you care about correctness, performance, reliability, and cost — not just features.
  • Comfortable integrating AI/ML capabilities into production systems.
  • Think critically about observability, evaluation, and failure modes for AI‑assisted features.
  • Care about responsible AI principles (privacy, transparency, safety) when shipping real user‑facing functionality.

Nice to have

  • Scala experience.
  • Strong observability background (Prometheus, Grafana, Splunk, Fluentbit).
  • Experience operating systems during high‑traffic or event‑driven spikes.
  • Prior exposure to AI‑assisted features, LLM APIs, or AI‑powered services in production.
  • Familiarity with monitoring AI/ML systems (telemetry, drift, performance signals).

What the JD emphasized

  • AI systems are only as good as the platforms they run on
  • AI-assisted features
  • observability
  • evaluation
  • failure modes
  • responsible AI principles

Other signals

  • AI-assisted features
  • production systems
  • observability
  • evaluation
  • failure modes
  • responsible AI principles