ML Architect

Sygna Solutions•Alpharetta, GA

About The Position

The ML Architect defines the target architecture and roadmap for machine learning on AWS and Databricks, and designs and implements scalable, secure, production-ready ML solutions. The role assesses existing Databricks implementations and recommends improvements, integrations, and platform capabilities. It establishes end-to-end ML workflows spanning data preparation, feature engineering, experimentation, model training and validation, deployment, monitoring, and retraining. The ML Architect implements MLOps practices using Databricks and MLflow, designs batch and real-time inference solutions, and advises teams on Databricks capabilities and integrations with AWS services. The role also establishes reusable architecture patterns, development standards, platform guardrails, and appropriate security, governance, and compliance practices. It optimizes workloads for scalability, reliability, performance, and cost, while partnering across technical and business teams to move ML use cases into production. Technical leadership, mentoring, documentation, and knowledge transfer are also part of the position.

Requirements

Extensive experience designing and delivering enterprise machine learning platforms and solutions.
Strong hands-on experience with Databricks in a production environment.
Deep knowledge of Databricks Lakehouse architecture and 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 cloud data security, IAM, encryption, networking, governance, and regulatory considerations.
Ability to translate business requirements into practical architecture and implementation plans.
Excellent communication skills and the ability to influence technical and non-technical stakeholders.

Responsibilities

Define the target architecture and roadmap for machine learning on AWS and Databricks.
Design and implement scalable, secure, production-ready machine learning solutions.
Assess the current Databricks implementation and recommend improvements, integrations, and platform capabilities.
Establish end-to-end ML workflows for data preparation, feature engineering, experimentation, model training, validation, deployment, monitoring, and retraining.
Implement MLOps practices with 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 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, including Amazon S3, IAM, KMS, SageMaker, Lambda, ECR, CloudWatch, and streaming services.
Define reusable architecture patterns, development standards, and platform guardrails.
Establish 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.