Machine Learning Engineer

Job Role: MLOps Engineer

Job location: Remote

Overview

We are seeking an experienced Machine Learning Engineer for a contract engagement supporting a large government systems integrator. This role focuses on designing, building, and operationalizing ML pipelines within a secure, FedRAMP-compliant cloud environment powered by Azure and Snowflake.

Responsibilities

• Design, develop, and maintain end-to-end ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment
• Build and optimize data workflows leveraging Snowflake as the primary data platform
• Deploy and manage ML workloads on Microsoft Azure, using services such as Azure Machine Learning, Azure Databricks, and Azure Data Factory
• Ensure all solutions comply with FedRAMP security requirements and follow government data-handling best practices
• Collaborate with data engineers, data scientists, and stakeholders to translate business requirements into scalable ML solutions
• Implement CI/CD practices for model versioning, testing, and automated retraining
• Monitor model performance in production and establish alerting and drift-detection mechanisms

Required Qualifications

• U.S. Citizenship (non-negotiable; required by the end customer)
• 3+ years of hands-on experience building and deploying ML pipelines in production environments
• Strong proficiency with Python and ML frameworks (scikit-learn, TensorFlow, PyTorch, or similar)
• Experience with Microsoft Azure cloud services (Azure ML, Databricks, Data Factory, Blob Storage, etc.)
• Experience working with Snowflake for data warehousing, feature stores, or ML data pipelines
• Familiarity with FedRAMP or equivalent government compliance frameworks (NIST 800-53, IL4/IL5, FISMA)
• Solid understanding of MLOps practices: model versioning, experiment tracking (MLflow, Weights & Biases), containerization (Docker), and orchestration (Airflow, Prefect, or similar)

Preferred Qualifications

• Experience supporting federal agencies or government integrators (e.g., Booz Allen, Leidos, SAIC, Deloitte, Accenture Federal, etc.)
• Familiarity with Azure Government Cloud (Azure Gov)
• Experience with Azure OpenAI Service and building LLM-powered applications (RAG pipelines, embeddings, prompt engineering)
• Exposure to Azure AI Services (formerly Cognitive Services) for vision, language, document intelligence, or speech workloads
• Familiarity with Azure AI Studio / Prompt Flow for LLM evaluation, orchestration, and deployment
• Experience with Azure Synapse Analytics as a complement to Snowflake-based data pipelines
• Experience with infrastructure-as-code tools (Terraform, Bicep, ARM templates)
• Knowledge of responsible AI principles, model explainability, and bias detection