ML Architect

This is a heavy-hitting architecture role where you will own the end-to-end machine learning roadmap on AWS and Databricks. If you enjoy untangling messy legacy setups and designing clean, scalable MLOps platforms from scratch, this contract is worth your time.

What You'll Actually Be Doing

You will spend your days bridging the gap between raw data and production-grade ML systems. Expect to audit existing Databricks implementations, set up bulletproof MLOps pipelines using MLflow, and guide teams on integrating AWS services like S3, IAM, and SageMaker. You'll be the technical authority ensuring that batch and real-time inference workloads actually scale without burning through the cloud budget.

The Core Tech Stack

Your bread and butter here will be Databricks and AWS, supported heavily by PySpark, MLflow, and Unity Catalog. The client needs someone who doesn't just know these tools on paper, but has battle scars from running them in production. You must be fluent in Delta Lake, Lakehouse monitoring, and automated model governance to keep their stakeholders happy.

Interview Expectations

Expect the interview panel to grill you on failure modes in distributed training and inference—be ready to explain how you handle data drift and model retraining at scale in a production environment. The hiring manager is secretly testing whether you can defend your architecture choices under strict security and cost constraints, so expect them to push back on your proposed AWS and Databricks integrations to see how pragmatic your designs really are.

Application Advice

Skip the generic tech buzzwords and tailor your resume to highlight concrete deployments involving Databricks Lakehouse, MLflow tracking, and AWS infrastructure. Make sure terms like Unity Catalog, PySpark, MLOps, and CI/CD pipelines are front and center so you easily clear their automated ATS filters.