Senior ML Platform Architect - AWS & Databricks

Sygna Solutions; Alpharetta (GA); On-site; USD 120,000 - 210,000; Full time

Job summary

Sygna Solutions seeks an experienced ML Platform Architect to define target architecture for enterprise ML on AWS, Databricks, and related services. You will design scalable, secure, production-grade ML pipelines and guide cross-functional teams.

The role emphasizes end-to-end ML workflows, model governance, CI/CD, and cost optimization, with a focus on Delta Lake, Unity Catalog, and real-time inference. Strong collaboration and communication are essential.

Qualifications

• Extensive experience designing and delivering enterprise ML platforms.
• Strong hands-on Databricks experience in production.
• Deep knowledge of Lakehouse architecture and ML capabilities.

Responsibilities

• Define target architecture and roadmap for ML on AWS and Databricks.
• Design scalable, secure, production-ready ML solutions.
• Assess Databricks implementation and recommend improvements.
• Establish end-to-end ML workflows from data prep to retraining.
• Implement MLOps with Databricks and MLflow including governance.
• Design batch and real-time inference solutions per requirements.
• Advise teams on Unity Catalog, Feature Engineering, and Lakehouse tools.
• Integrate Databricks with AWS services (S3, IAM, SageMaker, Lambda, etc.).
• Define reusable architecture patterns and platform guardrails.
• Establish security, data/model governance, and compliance practices.
• Optimize workloads for scalability, reliability, performance, cost.
• Partner with data scientists and engineers to productionize ML use cases.
• Provide leadership, mentoring, and knowledge transfer.

Skills

Databricks

AWS architecture

MLOps

Python

SQL

Spark/PySpark

Tools

MLflow

Unity Catalog

Delta Lake

Databricks Workflows

Job description

Sygna Solutions seeks an experienced ML Platform Architect to define target architecture for enterprise ML on AWS, Databricks, and related services. You will design scalable, secure, production-grade ML pipelines and guide cross-functional teams.

The role emphasizes end-to-end ML workflows, model governance, CI/CD, and cost optimization, with a focus on Delta Lake, Unity Catalog, and real-time inference. Strong collaboration and communication are essential.