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
Tags & focus areas
Used for matching and alerts on DevFound Contract Remote Machine Learning Data Science Mlops Data Engineer Ai