ML Administrator

Prescient Edge is seeking a ML Administrator to support a Federal Government client.

Please note that the availability of this position is contingent upon contract award.

Benefits:

At Prescient Edge, we believe that acting with integrity and serving our employees is the key to everyone's success. To that end, we provide employees with a best-in-class benefits package that includes:

• A competitive salary with performance bonus opportunities.
• Comprehensive healthcare benefits, including medical, vision, dental, and orthodontia coverage.
• A substantial retirement plan with no vesting schedule.
• Career development opportunities, including on-the-job training, tuition reimbursement, and networking.
• A positive work environment where employees are respected, supported, and engaged.

Description:

• ML Administrator (Manage knowledge graph metadata integration, data governance, and cataloging in Databricks Unity Catalog.)
• Proposed personnel possess the knowledge and capability to support Databricks platform administration and enterprise data architecture for DoD data-driven projects: Skilled in Databricks platform administration, including workspace management, cluster optimization, and Unity Catalog integration for secure data governance.
• Proficient in ETL pipeline development, Delta Lake architecture, and data Lakehouse optimization for DoD analytics and mission critical data workflows.
• Experienced in SysEngOps, DevSecOps, version control systems (Git), and CI/CD pipelines to streamline Databricks development and deployment.
• Knowledgeable in identity and access management (IAM), role-based access control (RBAC), and cloud security best practices across AWS, Azure, and GCP.
• Hands-on expertise in Python, SQL/NoSQL, Apache Spark, Databricks SQL, Terraform, and cloud native data services for large-scale data processing and analytics.
• Shall manage machine learning model versioning, lineage tracking, and compliance with governance policies, ensuring reproducibility and secure deployment.
• Shall implement and monitor ML infrastructure, optimizing compute resource allocation across cloud and on-premises environments.
• Shall submit the ML Model Governance & Deployment Report, summarizing model lifecycle management, performance tracking, and security compliance.