ML Engineer with Security Clearance

Build Scalable Data & ML Infrastructure · Design and implement medallion architecture (Bronze/Silver/Gold) using Databricks for reliable data processing and ML model training · Develop automated data pipelines that process structured and unstructured data from multiple sources into analytics-ready formats · Create robust ETL/ELT workflows using Apache Spark and modern data engineering practices for both batch and streaming data · Build and maintain data quality monitoring and validation systems across the entire data and ML lifecycle Drive ML Platform Excellence · Implement MLOps best practices including automated model training, validation, deployment, and monitoring using MLflow and Databricks workflows · Design scalable ML inference systems that handle high-volume, low-latency predictions in production environments · Create comprehensive monitoring and alerting systems for model performance, data drift, and system health · Build self-service ML capabilities that enable data scientists to deploy and monitor their own models efficiently Enable Advanced Analytics & Business Intelligence · Design and maintain data models that support both machine learning workloads and business intelligence requirements · Create integration points between ML systems and business intelligence platforms (Tableau, PowerBI, Qlik Sense) · Implement data governance standards and metadata management systems that ensure data quality and compliance · Collaborate with analysts and data scientists to optimize data architecture for both predictive modeling and reporting needs Ensure Data Quality & Governance · Implement comprehensive data governance frameworks including data lineage tracking, quality monitoring, and compliance controls · Design and maintain data catalogs and metadata management systems that enable efficient data discovery across the organization · Establish data quality standards and automated testing frameworks for both analytical and ML workloads · Work with stakeholders to define data definitions, business logic, and governance policies Integrate with Enterprise Systems · Connect Databricks-based systems with enterprise data warehouses, streaming platforms, and business applications · Collaborate with platform engineers to integrate ML systems with broader application architecture and infrastructure Required Skills – What You’ll Bring: · 5+ years of technical experience, including 3+ years building production data pipelines and ML infrastructure using distributed computing platforms like Databricks. · Strong data engineering skills in Python, PySpark, and Spark SQL with experience implementing medallion architecture and modern data platform patterns · Production ML systems experience including model deployment, monitoring, and MLOps practices in cloud environments · Data architecture expertise with experience designing scalable data processing systems and implementing data governance frameworks · Experience integrating with platforms such as Qlik, Tableau, PowerBI, MAVEN Smart System (Palantir), or similar. Deep expertise in distributed computing, performance optimization, and large-scale data processing using Databricks and Apache Spark · Advanced MLOps knowledge including automated retraining, model versioning, model testing frameworks, and production ML monitoring · Experience conducting regression analysis, and building predictive models for business applications with measurable impact · Advanced statistical knowledge including experimental design, hypothesis testing, causal inference, and statistical modeling techniques · Experience designing and building enterprise-level dashboards, reports, and self-service analytics platforms · Analytics platform knowledge including experience with Advana / MAVEN Smart Systems (Palantir Foundry) or similar enterprise analytics environments