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Radiance Technologies · 1 month ago

AI/ML Solutions Architect

Beavercreek, OH

Full-time

Hybrid

Senior Level

5+ years exp

Radiance Technologies is seeking a highly skilled AI/ML Solutions Architect to lead the design, development, and deployment of advanced artificial intelligence and machine learning solutions. The role involves working with cross-functional teams to create scalable AI/ML architectures that meet security and compliance standards for government and national security programs.

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Comp. & Benefits

No H1B

Security Clearance Required

U.S. Citizen Only

Responsibilities

Lead the design, development, and deployment of advanced artificial intelligence and machine learning solutions supporting mission-critical government and national security programs

Work closely with cross-functional teams including data scientists, software engineers, cloud architects, and mission stakeholders to translate complex operational requirements into scalable, secure, and high-performance AI/ML architectures

Design, integrate, and optimize full-lifecycle ML pipelines, from data ingestion and model training to deployment and monitoring, ensuring all solutions meet DoD and Intelligence Community security and compliance standards

Evaluate emerging technologies, develop technical roadmaps, and ensure deployed systems achieve long-term reliability, scalability, and mission success

Convey complex technical concepts to diverse audiences and integrate advanced AI/ML capabilities within secure environments

Deliver solutions that adhere to federal security and compliance standards

Qualification

AI/ML architecture patternsEnd-to-end AI/ML pipelinesCloud-based AI/ML platformsMachine learning frameworksMLOps principlesPython for AI/MLCloud-native technologiesModel lifecycle managementCommunication skillsCollaboration with teams

Required

Bachelor's Degree in Computer Science, Engineering, Physics, Statistics, Mathematics or a related field

5+ years of professional experience in AI/ML engineering, data science, machine learning operations (MLOps), in cloud environments

U.S. citizenship and ability to obtain/maintain TS/SCI security clearance

Demonstrated experience designing end-to-end AI/ML pipelines operating in classified environments (AWS C2S, Azure Government Secret)

Demonstrated experience architecting, implementing and operationalizing ML models and data pipelines in production environments across air-gapped and hybrid infrastructures

Hands-on experience with at least one major cloud-based AI/ML platform (Azure Machine Learning, AWS SageMaker, or Kubeflow on Kubernetes)

Proven background working with structured and unstructured data, feature engineering, and model lifecycle management

Experience collaborating with multidisciplinary technical teams and supporting customer-facing technical engagements

Deep understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and data processing tools (Spark, Pandas, Kafka)

Strong grasp of AI/ML architecture patterns, MLOps principles, including CI/CD for ML, model deployment, monitoring, and governance

Proficiency in Python for AI/ML development; familiarity with languages such as Java, Go, or C++ is a plus

Expertise with cloud-native and container technologies: Kubernetes, Docker, serverless architectures, and understanding of cloud security and zero trust best practices

Ability to translate mission needs into technical requirements and architect end-to-end AI/ML solutions

Excellent written and verbal communication skills, with proven ability to present complex AI/ML concepts to senior stakeholders

Preferred

Active TS/SCI or higher clearance

Professional certifications such as AWS Certified Solutions Architect / Machine Learning – Specialty

Azure Solutions Architect / Azure AI Engineer

CompTIA Security+, CISSP, or equivalent

Experience with advanced ML domains, such as federated learning, reinforcement learning, LLM fine-tuning, vector databases, or retrieval-augmented generation (RAG)

Familiarity with DoD and Intelligence Community data environments, including security constraints and RMF accreditation processes

Understanding of zero-trust architectures, secure data enclaves, and cross-domain solutions

Experience with model interpretability, bias mitigation, and Responsible AI best practices