Senior MLOps Engineer

Senior MLOps Engineer

Analytica is seeking a highly skilled Senior MLOps Engineer to lead the design, development, and deployment of machine learning operations infrastructure for defense applications. This role requires expertise in systems integration, ML pipeline development, and responsible AI practices within government environments. The position focuses on building scalable data and AI capabilities that support DoD mission-critical operations while ensuring compliance with responsible AI principles.

This position offers the opportunity to shape the future of AI and machine learning capabilities within the Department of Defense while ensuring responsible, ethical, and effective deployment of advanced technologies in support of national security missions.

Responsibilities:

• Serve as subject matter expert for systems integration supporting the design, development, and deployment of command-and-control products
• Lead networking and integration efforts for AI/ML systems deployed in operational and exercise environments
• Design and implement robust MLOps pipelines that support real-time decision-making capabilities for defense applications
• Collaborate with testing teams to ensure ML systems perform effectively during military exercises and operational scenarios
• Provide senior technical expertise in modeling, designing, and delivering advanced data tools and ML platforms for DoD-wide adoption
• Build and maintain scalable infrastructure to handle massive-scale video, image, structured and unstructured text data processing
• Develop automated data ingestion, preprocessing, and feature engineering pipelines that support diverse data types and formats
• Work with engineers, commercial vendors, government agencies, and academic partners to identify and implement cutting-edge ML capabilities
• Establish and maintain an enterprise-level advanced technology pipeline to accelerate AI/ML adoption across the agency
• Implement data quality monitoring, validation, and governance frameworks to ensure high-quality training datasets
• Design and optimize ML model training, testing, and deployment workflows that reduce implementation cycles
• Develop automated testing and validation processes for ML models to ensure reliability and performance in operational environments
• Support the development of optimization models for government engagement with external partners and technology vendors
• Create and maintain integration workflows that leverage external capabilities while maintaining security and compliance standards
• Develop vendor assessment criteria and integration protocols for AI/ML technologies and services
• Create, iterate, and publish comprehensive guidance materials for data science and AI development practices
• Develop and maintain coding standards and best practices documentation for ML development teams
• Establish documentation standards for model versioning, experiment tracking, and deployment procedures
• Design and implement data integration solutions that support enterprise data mesh architecture
• Develop real-time data feed integration capabilities for live operational data streams
• Build APIs and microservices that enable seamless data sharing across DoD systems and applications
• Ensure data interoperability standards compliance across diverse defense systems and platforms
• Lead the implementation and management of DoD responsible artificial intelligence programs and initiatives
• Integrate responsible AI tools, frameworks, and assessment methodologies into existing and new use cases
• Develop comprehensive performance metrics and evaluation frameworks for AI/ML projects and tools
• Conduct regular assessments of AI system outcomes, bias detection, and ethical compliance
• Prepare executive-level reports and briefings on responsible AI implementation progress and recommendations
• Establish MLOps monitoring and observability solutions for production ML systems
• Develop automated model performance tracking, drift detection, and retraining pipelines
• Create dashboards and reporting tools that provide visibility into ML system performance and reliability
• Conduct post-deployment analysis and continuous improvement of ML operations processes

Required Qualifications:

• Bachelor's degree in Computer Science, Data Science, Machine Learning, or related technical field;
  Master's degree preferred
• Minimum 7-10 years of experience in MLOps, Dev Ops, or related systems engineering roles
• Strong expertise in ML frameworks (Tensor Flow, PyTorch, Scikit-learn), containerization (Docker, Kubernetes), and cloud platforms
• Experience with data pipeline tools (Apache Airflow, Kubeflow, MLflow) and big data technologies (Spark, Hadoop)
• Knowledge of government compliance requirements, security frameworks, and responsible AI principles

Preferred Qualifications:

• Experience with defense or intelligence community AI/ML projects
• Certifications in cloud platforms (AWS ML, Azure AI, GCP ML) and MLOps tools
• Knowledge of responsible AI frameworks and bias detection methodologies
• Experience with real-time data processing…