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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - Senior System Integrator - **Company:** 3 Reasons Consulting - **Location:** San Diego, CA, United States - **Experience:** Expert - **Salary:** $120,000.0 - $135,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Windows, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Computer Vision, Automation of Tests, Cloud Computing, Configuration Management, Cyber Security, Databases, Computer Engineering, Continuous Integration, Information Engineering, Linux, Distributed Systems, Elasticsearch, Python (Programming Language), Network Layer, PostgreSQL, Machine Learning, MongoDB, Natural Language Processing, Octopus Deploy, Ansible, Tensorflow, Security Software, Software Deployment, Software Engineering, Data Streaming, System Testing, Systems Integration, Software Vulnerability Management, AI Infrastructure, Software Organization, Enterprise Software Applications, Performance Testing, Data Ingestion, Pytorch, Large Language Models, Generative AI, Gitlab, SC Clearance, Containerization, AI Platforms, Scikit Learn, Integration Tests, Kubernetes, Infrastructure Automation Frameworks, Information Technology, HuggingFace, Enterprise Integration, Integration Frameworks, Machine Learning Operations, Hardware Infrastructure, Restful APIs, Terraform, Software Version Control, Data Pipelines, Devsecops, Docker, Jenkins, Vulnerability Analysis, Microservices - **Published:** September 29, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9199297/ai-engineer-senior-system-integrator ## About the Role Security+ or other DoD 8570/8140-compliant certification. Active Secret clearance as required by the contract. 3+ years of experience in software engineering, systems integration, AI/ML engineering, or a related technical field. Demonstrated experience integrating complex software, data, and infrastructure components. Strong understanding of AI/ML concepts, model lifecycle management, and production AI systems. Experience with Python and modern software development practices. Experience with REST APIs, microservices, databases, and distributed systems. Experience with containers and Kubernetes or comparable orchestration platforms. Experience with CI/CD and DevSecOps methodologies. Strong Windows/Linux systems experience. Experience troubleshooting complex systems across application, infrastructure, data, and network layers. Excellent technical documentation and communication skills. Preferred Qualifications Bachelor's degree in Computer Science, Computer Engineering, Artificial Intelligence, Data Science, or related technical discipline. Experience supporting Department of Defense or Navy programs. Experience operationalizing generative AI, machine learning, computer vision, natural language processing, or other advanced AI capabilities. Experience with PyTorch, TensorFlow, scikit-learn, Hugging Face, or comparable AI/ML frameworks. Experience with LLMs, RAG architectures, vector databases, embeddings, and AI agents. Experience with GPU infrastructure and accelerated computing. Experience with MLflow, Kubeflow, Airflow, or comparable MLOps platforms. Experience with Docker, Kubernetes, Helm, GitLab, Jenkins, Argo CD, Terraform, or Ansible. Experience with PostgreSQL, MongoDB, Elasticsearch/OpenSearch, or vector databases. Familiarity with AI security, responsible AI, model governance, and AI/ML vulnerability management. Experience with NIST AI Risk Management Framework or comparable AI governance/security frameworks. Familiarity with RMF, NIST 800-53, DISA STIGs, and DoD 8140/8570 requirements. Security+ or other DoD-compliant cybersecurity certification. What Success Looks Like ## Description The Senior AI Engineer - System Integrator will serve as a technical bridge between AI/ML development, infrastructure, software engineering, data engineering, cybersecurity, and mission stakeholders. The successful candidate will be responsible for integrating AI capabilities into operational environments and ensuring that AI/ML solutions can be securely deployed, monitored, maintained, and scaled. This is a hands-on engineering position for an experienced technologist who understands both AI/ML systems and the infrastructure required to operationalize them. Key Responsibilities AI/ML Systems Integration Design, integrate, deploy, and maintain AI/ML capabilities within enterprise and mission environments. Translate AI/ML requirements into scalable technical architectures and integration solutions. Integrate machine learning models, data pipelines, APIs, applications, and infrastructure into production environments. Support the transition of AI/ML prototypes and research efforts into reliable operational capabilities. Evaluate emerging AI technologies and recommend solutions aligned with mission and technical requirements. Troubleshoot complex integration issues across AI applications, infrastructure, data, networking, and security components. AI Engineering & MLOps Support the development and implementation of MLOps pipelines for model development, testing, deployment, monitoring, and lifecycle management. Automate model deployment and operational workflows using modern DevSecOps practices. Implement version control, model versioning, automated testing, and reproducible deployment processes. Monitor model and system performance and support model lifecycle management. Integrate AI/ML workloads with containerized and cloud or hybrid infrastructure. Support deployment of AI workloads using Kubernetes and container technologies. System Integration & Architecture Develop and maintain technical architectures supporting AI-enabled applications. Integrate AI services with existing enterprise systems, applications, databases, APIs, and infrastructure. Evaluate system dependencies, interfaces, data flows, and performance requirements. Develop and maintain system integration documentation, architecture diagrams, interface specifications, and technical procedures. Support system testing, integration testing, performance testing, and production deployment. Identify integration risks and develop mitigation strategies. Data & AI Infrastructure Work with data engineers and AI/ML teams to support data ingestion, processing, transformation, and availability. Integrate AI workloads with structured and unstructured data sources. Support scalable compute, storage, networking, and GPU infrastructure required for AI workloads. Optimize AI/ML environments for performance, scalability, reliability, and resource utilization. Support data and model pipelines across development, test, and production environments. DevSecOps & Automation Integrate AI/ML capabilities into secure CI/CD and DevSecOps pipelines. Automate infrastructure provisioning, configuration, testing, and deployment. Utilize Infrastructure as Code and configuration-management practices. Implement automated security, vulnerability, and compliance checks throughout the development lifecycle. Collaborate with DevSecOps engineers to establish repeatable and secure deployment processes. Cybersecurity & DoD Compliance Ensure AI/ML systems and supporting infrastructure comply with applicable DoD cybersecurity requirements. Support Risk Management Framework (RMF), NIST 800-53, DISA STIG, and security authorization activities. Implement security controls around AI applications, models, APIs, containers, data, and infrastructure. Support vulnerability assessment and remediation activities. Maintain technical and security documentation required for authorization and operational support. Collaborate with cybersecurity teams, ISSOs, ISSMs, and system administrators to address security requirements. Technical Leadership Serve as a senior technical advisor for AI system integration initiatives. Provide technical guidance to engineers, developers, data scientists, and infrastructure teams. Participate in architecture reviews, technical design sessions, and engineering working groups. Communicate complex AI and technical concepts to both technical and non-technical stakeholders. 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