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.
Analytica has been recognized by Inc. Magazine as one of the fastest\-growing 250 businesses in the US for 3 years. We work with U.S. government clients in health, civilian, and national security missions to build better technology products that impact our day\-to\-day lives. The company offers competitive compensation with opportunities for bonuses, employer\-paid health care, training and development funds, and 401k match.
Responsibilities:
Systems Integration \& Command\-and\-Control Development:
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
Enterprise Data \& ML Pipeline Architecture:
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
Advanced Technology Pipeline Development:
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
Partnership Optimization \& Process Development:
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
Documentation \& Knowledge Management:
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
Enterprise Data Integration \& Interoperability:
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
Responsible AI Implementation \& Governance:
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
Performance Monitoring \& Evaluation:
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, DevOps, or related systems engineering roles
Strong expertise in ML frameworks (TensorFlow, 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 and streaming technologies
Familiarity with DoD enterprise architecture and data sharing standards
Current security clearance or ability to obtain required clearance level