Senior AI Engineer

IntelliGenesis is seeking a Senior AI Engineer to lead the design, development, and deployment of production-grade AI systems supporting mission-critical intelligence operations. This role focuses on transitioning AI/ML capabilities from concept to operational environments, including classified and resource-constrained settings. 

A day in the life includes architecting scalable AI pipelines, deploying models to edge and cloud environments, integrating AI into cybersecurity workflows, and mentoring junior engineers. You will work closely with cyber operators, software engineers, and infrastructure teams to deliver impactful, real-world AI capabilities—not just research prototypes. 

The team dynamic is highly collaborative and mission-driven, consisting of AI engineers, cyber SMEs, and platform engineers working in agile sprints. This role serves as a technical leader and mentor, guiding best practices in MLOps, DevSecOps, and secure AI deployment. 

• Lead end-to-end AI system design, development, and deployment 
• Architect and implement scalable MLOps pipelines for training, validation, and deployment 
• Deploy AI/ML models to cloud, on-prem, and edge environments 
• Integrate AI capabilities into cybersecurity tools and operational workflows 
• Ensure system security, including adversarial robustness and secure model deployment 
• Collaborate with cross-functional teams (cyber, infrastructure, software engineering) 
• Mentor mid-level engineers and provide technical oversight 
• Rapidly prototype AI solutions and transition them into production systems 
• Ensure compliance with DoD Risk Management Framework (RMF) requirements 

• Must be a U.S. Citizen
• Active TS/SCI Clearance and Polygraph required 
• 8+ years of experience in AI/ML engineering, software engineering, or related field 
• Bachelor’s degree in Computer Science, Engineering, or related field
• Strong experience deploying AI/ML models into production environments
• Expertise in Python and at least one additional language (e.g., C++, Go) 
• Experience with MLOps tools (e.g., Kubernetes, Docker, MLflow, Kubeflow, VMWare, Tanzu) 
• Experience with cloud and hybrid infrastructure (AWS, Azure, or DoD cloud environments) 
• Knowledge of DevSecOps and infrastructure-as-code (e.g., Terraform, Ansible) 
• Experience with model serving, monitoring, and lifecycle management 
• Familiarity with cybersecurity concepts and secure system design 
• Experience working in classified or regulated environments (DoD/IC preferred) 

• Master's degree
• Experience with adversarial machine learning and AI security 
• Background in cyber operations or network traffic analysis 
• Experience deploying models in edge or disconnected environments 
• Familiarity with large language models (LLMs) and generative AI systems 
• Knowledge of secure enclaves and confidential computing 
• Prior experience supporting DoD or Intelligence Community missions 
• Relevant certifications (e.g., AWS Certified Solutions Architect, Security+, CISSP) 

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Compensation ranges encompass a total compensation package and are a general guideline only and not intended as a guaranteed and/or implied final compensation or salary for this job opening. Determination of official compensation or salary relies on several different factors including, but not limited to: level of position, complexity of job responsibilities, geographic location, candidate’s scope of relevant work experience, educational background, certifications, contract-specific affordability, organizational r