AI Engineer
Target Labor Category: Junior, Intermediate, Senior
Position Location: Chantilly, VA (Customer Site)
Clearance Required: TS/SCI with Polygraph
Position Overview
Stratos Solutions has an opportunity for an AI Engineer to support an Intelligence Community (IC) Space customer in Chantilly, VA. The selected candidate will operate within a collaborative government-contractor team environment developing, integrating, and deploying artificial intelligence and machine learning capabilities that support mission-critical national security programs.
This role focuses on translating complex mission and business challenges into operational AI-enabled solutions. The successful candidate will design, develop, and deploy production-grade AI/ML systems and platforms, supporting the full AI lifecycle from data ingestion and model development through deployment and operational sustainment. The position requires expertise in scalable AI architectures, software development lifecycle integration, and advanced analytics techniques including deep learning, natural language processing, and computer vision.
If you are passionate about building operational AI capabilities that directly support mission advantage, we encourage you to apply.
About Stratos Solutions
Stratos Solutions delivers mission-focused engineering, acquisition, and operations support to the Intelligence Community and national security space enterprise. As an employee-owned company, Stratos combines technical excellence, operational agility, and a deep commitment to customer mission success. Our teams work at the forefront of space and intelligence innovation supporting programs critical to national security.
Responsibilities
The AI Engineer will support government customers in designing, developing, integrating, and deploying advanced AI/ML capabilities across enterprise and mission system environments.
Responsibilities include, but are not limited to:
AI Solution Development & Engineering
Understand mission and business challenges and identify appropriate AI, machine learning, or cognitive computing solutions.
Design and implement AI-driven architectural strategies aligned with mission objectives and product requirements.
Develop machine learning-based software supporting domain-specific applications such as:
Image recognition and object detection
Natural language processing and text analytics
Pattern recognition and anomaly detection
Autonomous or decision-support analytic systems
Build AI models from initial concept through operational deployment.
Apply data mining, pattern matching, and pattern recognition techniques to mission datasets.
AI Platform & Production Deployment
Develop and support AI platforms and enterprise AI enablement initiatives.
Deploy AI/ML models into production environments using scalable and resilient architectures.
Build APIs and integration services to operationalize AI model outputs into mission workflows and user applications.
Support full AI lifecycle activities including model validation, performance monitoring, sustainment, and optimization.
Identify transfer learning opportunities and assist with development of new training datasets.
Data Engineering & Infrastructure Integration
Build and maintain data ingestion, data transformation, and model training pipelines.
Integrate AI technologies into existing mission and enterprise infrastructure ensuring scalability, reliability, and performance.
Support development of AI-enabled software solutions within modern DevSecOps and software development lifecycle environments.
Cross-Functional Collaboration & Technical Leadership
Collaborate with data scientists, software developers, product managers, and mission stakeholders to identify, prioritize, and implement AI capabilities.
Assist stakeholders in understanding AI capabilities, limitations, and appropriate use cases.
Research emerging AI technologies and recommend platforms, frameworks, and implementation approaches.
Solve complex technical challenges and present AI solution strategies to government customers and program leadership.
Research & Innovation
Stay current on emerging AI and machine learning technologies relevant to mission applications.
Research and develop improvements to machine learning algorithms and model architectures.
Evaluate commercial and open-source AI technologies for mission applicability.
Support compliance with applicable regulatory, security, and industry standards throughout the AI lifecycle.
Required Qualifications
Education & Clearance
Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, or related technical field
(Relevant experience may substitute for degree requirements)
Active TS/SCI with Polygraph
Technical & Functional Skills
Experience designing and developing machine learning and AI-enabled applications
Experience with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, or similar)
Strong software development experience using Python
Experience with one or more additional programming languages such as C++, Java, Go/Golang, Scala, or similar
Experience deploying AI models into production environments
Experience developing data pipelines and model training workflows
Understanding of software development lifecycle and DevSecOps integration
Strong analytical, computational, and problem-solving skills
Desired Skills / Qualifications
Experience supporting Intelligence Community or DoD space programs (NRO experience highly desired)
Experience with NLP, deep learning, or computer vision applications
Experience deploying AI capabilities in cloud and hybrid computing environments
Experience with containerization and orchestration technologies (Docker, Kubernetes, etc.)
Experience building scalable AI platforms or AI service architectures
Experience working with large-scale distributed data environments
Experience supporting transfer learning and model reuse strategies
Experience developing operational AI APIs and microservices
Experience evaluating commercial AI technologies and emerging AI frameworks
Familiarity with Responsible AI, AI governance, or model validation frameworks
Experience working in high-performance computing (HPC) environments
