AINative Platform Engineer Media Analytics

The Opportunity

Adobe Media Analytics powers some of the world’s largest digital experiences — from major live events to always‑on streaming platforms. Our systems operate at a massive scale, with strict requirements around latency, reliability, cost efficiency, and correctness. 

Media is a mature, critically important product. It doesn’t tolerate experimentation in production — which makes building AI‑assisted analytics and workflows managed by agents especially challenging, and especially interesting.  

We seek an AI‑Native 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 — you’ll feel at home here. 

What You’ll Do

• Build and operate cloud‑native, distributed information and application platforms that power analytics and AI‑assisted features at scale.  

• Design and evolve event‑driven and streaming architectures (Kafka‑based and similar) that remain reliable under peak traffic and failure conditions. 

• Translate product and business requirements into robust, production‑ready systems, not prototypes. 

• Improve existing architectures by identifying operational risks, performance bottlenecks, and cost inefficiencies. 

• Contribute across the full lifecycle: design, implementation, code review, testing, deployment, monitoring, and on‑call. 

• Participate in an on‑call rotation and help improve incident response, observability, and system resilience. 

• Enable AI‑assisted 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. 

What you'll need to succeed

This is not a pure AI role and not a traditional big‑data role. 
We’re looking for engineers who understand that AI systems are only as good as the platforms they run on. 

You likely have: 

• 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). 

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

AI‑Related Expectations (Important) 

• Be 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).