ML Architect - Remote - only - W2/own corporation

Role: ML Architect

Duration: 3+ months

Allocation: 40 hrs per week

Location: Remote

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.