Senior AI Systems Integrator - Secure MLOps & Kubernetes

DataJobs; San Diego (CA); On-site; USD 120,000 - 135,000; Full time

Benefits offered by this job

Short/Long Term Disability

Basic Life Insurance

Direct Payroll Deposit

Leave Accrual

Holidays

401(k) Match

Additional Life Insurance

401(k)

Medical Coverage

Dental Coverage

Vision Care Plan

Flexible Spending Account Plan

Job summary

3 Reasons Consulting in San Diego, CA seeks a Senior AI Engineer specializing in system integration to deploy secure enterprise AI capabilities across AI/ML, infrastructure, data, cybersecurity, and mission stakeholders. This hands-on role connects AI/ML engineering with DevSecOps and MLOps for a complete lifecycle from prototype to operational capability.

The role emphasizes integrating AI services with enterprise systems, containers, cloud/hybrid infrastructure, and DoD cybersecurity

Qualifications

• Security+ or 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.

Responsibilities

• 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.
• Support 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.
• Develop and maintain technical architectures for 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.
• Collaborate 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.
• Integrate AI/ML capabilities into secure CI/CD and DevSecOps pipelines.
• Automate infrastructure provisioning, configuration, testing, and deployment, using IaC 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.
• Ensure AI/ML systems and supporting infrastructure comply with DoD cybersecurity requirements, including RMF, NIST 800-53, and DISA STIG 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.
• Serve as a senior technical advisor for AI system integration initiatives and 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 technical and non-technical stakeholders.
• Help establish engineering standards, best practices, and repeatable processes for AI system integration.

Skills

Python

AI/ML engineering

Kubernetes

CI/CD

DevSecOps

Security+ / DoD 8570

Active Secret clearance

REST APIs

System integration

Windows/Linux

Education

Bachelor's degree in Computer Science/Engineering/AI/Data Science

Tools

Docker

Helm

Terraform

Ansible

Job description

3 Reasons Consulting in San Diego, CA seeks a Senior AI Engineer specializing in system integration to deploy secure enterprise AI capabilities across AI/ML, infrastructure, data, cybersecurity, and mission stakeholders. This hands-on role connects AI/ML engineering with DevSecOps and MLOps for a complete lifecycle from prototype to operational capability.

The role emphasizes integrating AI services with enterprise systems, containers, cloud/hybrid infrastructure, and DoD cybersecurity