Job Description & Details
This is a heavy-hitting AI/ML Architect role focused on designing complex, distributed systems from scratch in an onsite environment. If you have 15+ years under your belt and are tired of just pushing microservices around, this position throws you straight into the deep end of enterprise GenAI and MLOps infrastructure.
What You'll Actually Be Doing
You will spend your days bridging the gap between high-level business strategy and gritty systems architecture. Expect to design end-to-end machine learning pipelines, wrangle massive distributed data flows, and build out robust LLM applications using RAG and fine-tuning techniques. The reality here is dealing with ambiguity and tight timelines, meaning you'll need to unblock engineering teams while managing expectations of senior stakeholders who want AI results yesterday.
The Core Tech Stack
Python is non-negotiable here, serving as the glue for everything from microservices to orchestration tools like Kubeflow, MLflow, or Apache Airflow. You need serious fluency in cloud ecosystems—whether that's AWS, Azure, or GCP—alongside containerization with Docker and Kubernetes. Because you'll be handling heavy data loads, deep familiarity with Spark, Kafka, and enterprise data lakes is a strict prerequisite for keeping these distributed AI systems from collapsing under their own weight.
Interview Expectations
Expect the hiring panel to test your battle-scars by asking how you would architect a real-time RAG system handling millions of concurrent requests while keeping latency low and hallucination rates minimal. They are secretly testing whether you understand the actual financial and operational bottlenecks of running LLMs in production, rather than just reciting textbook definitions. You'll also likely be grilled on a time you had to handle severe technical ambiguity and untangle a failing MLOps pipeline under an aggressive deadline.
Application Advice
To get past the ATS and grab the attention of the engineering leads, drop the generic summaries and front-load your resume with hard numbers around distributed systems, model deployments, and cloud scale. Make sure exact keywords like MLOps, Kubeflow, RAG, fine-tuning, Kafka, and Kubernetes are explicitly tied to projects where you drove the architecture from conception to production. Frame your experience around stakeholder influence and resilience, showing them you can lead a room just as well as you can write the code.