D
AI Engineer – Senior System Integrator
DataJobs
1 hour ago
Full-time
On-site
San Diego, California, United States
3 Reasons Consulting is seeking a Senior AI Engineer specializing in system integration to deploy advanced AI capabilities within secure enterprise and mission environments supporting the
Naval Health Research Center (NHRC)
in
San Diego, CA . This is a hands-on role that connects AI/ML engineering, infrastructure, data engineering, cybersecurity, and mission stakeholders across the full lifecycle from prototype to operational capability.
Role 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 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.
Ensure AI/ML systems and supporting infrastructure comply with applicable
DoD cybersecurity requirements , including support for
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, including 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.
Required Qualifications
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.
Technologies and Tools
AI/ML, MLOps, DevSecOps, CI/CD
Python, REST APIs, microservices
Kubernetes, Docker, Helm, containers
Infrastructure as Code, configuration-management, Terraform, Ansible
RMF, NIST 800-53, DISA STIG
Security+, Windows, Linux
PostgreSQL, MongoDB, Elasticsearch/OpenSearch
PyTorch, TensorFlow, scikit-learn, Hugging Face, LLMs
RAG architectures, vector databases, embeddings, AI agents
GPU infrastructure, MLflow, Kubeflow, Airflow
GitLab, Jenkins, Argo CD
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.
Compensation and Location
Location:
San Diego, CA (onsite)
Salary:
USD 120,000 - 135,000 per year
Employment Type:
Full-Time On-site
Clearance:
Active Secret Clearance required based on contract requirements
Benefits
Short/Long Term Disability
Basic Life Insurance
Direct Payroll Deposit
Leave Accrual
Holidays
401(k) Match
Additional (Voluntary) Life Insurance
401(k)
Medical CoverageDental Coverage
Vision Care Plan
Flexible Spending Account Plan
#J-18808-Ljbffr
Naval Health Research Center (NHRC)
in
San Diego, CA . This is a hands-on role that connects AI/ML engineering, infrastructure, data engineering, cybersecurity, and mission stakeholders across the full lifecycle from prototype to operational capability.
Role 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 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.
Ensure AI/ML systems and supporting infrastructure comply with applicable
DoD cybersecurity requirements , including support for
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, including 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.
Required Qualifications
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.
Technologies and Tools
AI/ML, MLOps, DevSecOps, CI/CD
Python, REST APIs, microservices
Kubernetes, Docker, Helm, containers
Infrastructure as Code, configuration-management, Terraform, Ansible
RMF, NIST 800-53, DISA STIG
Security+, Windows, Linux
PostgreSQL, MongoDB, Elasticsearch/OpenSearch
PyTorch, TensorFlow, scikit-learn, Hugging Face, LLMs
RAG architectures, vector databases, embeddings, AI agents
GPU infrastructure, MLflow, Kubeflow, Airflow
GitLab, Jenkins, Argo CD
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.
Compensation and Location
Location:
San Diego, CA (onsite)
Salary:
USD 120,000 - 135,000 per year
Employment Type:
Full-Time On-site
Clearance:
Active Secret Clearance required based on contract requirements
Benefits
Short/Long Term Disability
Basic Life Insurance
Direct Payroll Deposit
Leave Accrual
Holidays
401(k) Match
Additional (Voluntary) Life Insurance
401(k)
Medical CoverageDental Coverage
Vision Care Plan
Flexible Spending Account Plan
#J-18808-Ljbffr