Senior AI Cloud Engineer with Security Clearance

Senior / Lead Cloud AI Engineer Overview
We’re seeking a Senior/Lead Cloud AI Engineer to design, build, and operate secure, mission-ready AI capabilities in federal cloud environments. This role blends cloud engineering, MLOps, and AI/ML engineering to deliver scalable, compliant solutions—from data pipelines and model training to deployment, monitoring, and continuous improvement—often in regulated or classified settings.

Key Responsibilities

• Cloud & Platform Engineering (AI-Ready Foundations)

• Design, implement, and maintain cloud infrastructure and platform services (AWS, Azure, or GCP) that support AI/ML workloads in secure federal environments.

• Build and manage Infrastructure-as-Code (IaC) and automated configuration for repeatable environments using tools such as Terraform or native cloud templates.

• Develop and maintain CI/CD pipelines enabling automated build, test, and deployment of cloud and AI services.

• Implement containerized and orchestrated runtime environments for AI services using Docker and Kubernetes/Open Shift.

• Engineer reliability, scalability, and performance through automation, monitoring, and operational best practices. AI / ML Engineering & MLOps

• Develop and deploy AI/ML solutions, including traditional machine learning and generative AI use cases.

• Design and operate production-grade MLOps pipelines for model training, testing, deployment, versioning, and rollback.

• Implement monitoring for model performance, drift, bias, and operational health.

• Integrate AI capabilities with enterprise systems, APIs, and cloud-native data pipelines.

• Support responsible AI practices, governance, and lifecycle management. Security, Compliance, and Federal Delivery

• Partner with cybersecurity and compliance teams to implement security controls and support federal accreditation processes.

• Produce technical documentation and evidence artifacts supporting authorization to operate (ATO) and continuous monitoring.

• Implement secure identity and access management, logging, and auditability aligned with federal standards.

• Lead technical solutioning efforts and mentor junior engineers.

• Communicate complex technical solutions clearly to both technical and non-technical stakeholders.

Required Qualifications

• 8+ years of experience in cloud engineering, Dev Ops, platform engineering, or systems engineering, with senior-level responsibility.

• 3+ years of hands-on experience delivering AI/ML solutions and operating MLOps pipelines in production.

• Strong experience with at least one major cloud provider (AWS, Azure, or GCP).

• Hands-on experience with CI/CD automation, Infrastructure-as-Code, and Dev Sec Ops  practices.

• Proficiency with container technologies (Docker) and orchestration platforms (Kubernetes or Open Shift).

• Strong scripting or programming experience (Python strongly preferred).

• Experience implementing logging, monitoring, alerting, and audit controls in regulated environments.

Preferred Qualifications

• Experience supporting federal or highly regulated cloud environments (e.g., Gov Cloud, classified or restricted partitions).

• Familiarity with federal cybersecurity frameworks and compliance activities.

• Experience modernizing legacy systems into cloud-native, AI-enabled architectures.

• Background building AI platforms, including model hosting, vector search, retrieval-augmented generation (RAG), and orchestration frameworks. Clearance / Work Environment
Ability to obtain and maintain a federal security clearance; active clearance may be required depending on the program.
Onsite or hybrid work may be required based on mission and program needs. Certifications (Preferred)

• Cloud certifications (AWS, Azure, or GCP).

• Security baseline certifications may be required on some programs. Example Technologies

• Cloud: AWS, Azure, GCP

• IaC:
Terraform, Cloud Formation, ARM

• CI/CD:
Git Lab CI, Git Hub Actions, Azure Dev Ops

• Containers:
Docker, Kubernetes, Open Shift

• AI/ML:
PyTorch, Tensor Flow, managed cloud AI services

• Observability/Security:
Centralized logging, monitoring, audit trails, policy enforcement Education
Bachelor’s degree in Computer Science, Engineering, or a…