ML Architect

Sygna Solutions; Alpharetta (GA); Vor Ort; USD 120.000 - 210.000; Vollzeit

Zusammenfassung

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.

Qualifikationen

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

Aufgaben

• 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.

Kenntnisse

Databricks

AWS architecture

MLOps

Python

SQL

Spark/PySpark

Tools

MLflow

Unity Catalog

Delta Lake

Databricks Workflows

Jobbeschreibung

Key Responsibilities:

• Define the target architecture and roadmap for machine learning on AWS and Databricks.


• Design and implement scalable, secure, and production-ready ML solutions.


• Assess the current Databricks implementation and recommend improvements, integrations, and platform capabilities.


• Establish end-to-end ML workflows covering data preparation, feature engineering, experimentation, model training, validation, deployment, monitoring, and retraining.


• Implement MLOps practices using Databricks and MLflow, including experiment tracking, model management, automated deployment, governance, and monitoring.


• Design batch and real-time model-inference solutions based on business and technical requirements.


• Advise teams on the effective use of Databricks capabilities, including Unity Catalog, Feature Engineering, Model Serving, Lakehouse Monitoring, Delta Lake, Delta Live Tables/Lakeflow, and Databricks Workflows.


• Integrate Databricks with relevant AWS services, such as Amazon S3, IAM, KMS, SageMaker, Lambda, ECR, CloudWatch, and streaming services.


• Define reusable architecture patterns, development standards, and platform guardrails.


• Establish appropriate security, access-control, data-governance, model-governance, and compliance practices.


• Optimize workloads for scalability, reliability, performance, and cost.


• Partner with data scientists, data engineers, platform teams, security teams, and business stakeholders to move ML use cases from concept into production.


• Provide technical leadership, mentoring, documentation, and knowledge transfer to internal teams.

Required Qualifications:

• Extensive experience designing and delivering enterprise machine learning platforms and solutions.


• Strong hands-on experience with Databricks in a production environment.


• Deep knowledge of the Databricks Lakehouse architecture and its machine learning capabilities.


• Strong experience with MLflow, including experiment tracking, model lifecycle management, deployment, and governance.


• Extensive AWS architecture and implementation experience.


• Strong understanding of MLOps, CI/CD, infrastructure as code, automated testing, observability, and model monitoring.


• Experience deploying both batch and real-time inference workloads.


• Proficiency in Python and SQL, with experience using Apache Spark/PySpark.


• Experience with feature engineering, model evaluation, drift detection, explainability, retraining, and responsible AI practices.


• Strong knowledge of data security, IAM, encryption, networking, governance, and regulatory considerations in cloud environments.


• Ability to translate business requirements into practical architecture and implementation plans.


• Excellent communication skills and the ability to influence technical and non-technical stakeholders.