Cloud AI Engineer

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $104k - $166k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 AI Engineer

🦅 H1B Visa Sponsor

This company has sponsored H1B visas in the past.

👻 Ghost score 0%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Peraton

WebsiteLinkedInAll Job Openings

10,000+ employees

💼 Consulting

🏥 Healthcare

📦 Logistics

Consulting • Healthcare • Logistics

Peraton is a mission-focused enterprise that supports national security initiatives through advanced IT and cyber services. They provide capabilities in areas such as cyber defense, cloud operations, engineering, and intelligence. With a commitment to solving complex challenges, Peraton integrates data-driven technologies to ensure mission success for their military and government clients.

📋 Description

• Build, train, and deploy machine learning models using managed AI/ML services across AWS, Azure, GCP, and OCI • Develop and maintain ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment • Implement model-serving infrastructure, including real-time inference endpoints, batch prediction workflows, and API integrations • Integrate large language models and generative AI capabilities into government applications with guardrails and compliance controls • Design and implement cloud-native ETL, data lake, and feature-store workflows • Prepare structured and unstructured training datasets while ensuring data quality, lineage, and governance • Optimize data pipelines for performance, cost, and reliability across cloud platforms • Monitor deployed models for performance degradation, data drift, and bias • Troubleshoot AI/ML workload issues, including training failures, inference latency, and resource utilization • Optimize cloud resource usage and costs, including GPU/accelerator allocation and spot/preemptible instances • Collaborate with data scientists, application developers, and infrastructure engineers to operationalize AI/ML solutions • Document AI/ML architecture decisions, deployment procedures, and operational runbooks • Support service delivery metrics and reporting with the Service Delivery Manager and ISR Product Owner • Follow Change Management procedures for production AI/ML deployments

🎯 Requirements

• Bachelor's degree and 5 years of experience, or an Associate's degree and 7 years of experience, or a high school diploma/equivalent and 9 years of experience • Must be a U.S. Citizen • Ability to obtain and maintain a DHS Public Trust • 3 to 5 years of experience in AI/ML engineering, data engineering, or applied machine learning using cloud-based technologies • Hands-on experience with managed AI/ML services on at least two cloud platforms: AWS, Azure, GCP, or OCI • Proficiency in Python and experience with TensorFlow, PyTorch, scikit-learn, or equivalent technologies • Experience designing, building, deploying, and maintaining ML pipelines and model-serving infrastructure in production cloud environments • Experience with cloud-based AI services, including generative AI, large language models, machine learning platforms, or related AI capabilities • Familiarity with responsible AI, model governance, data governance, and federal compliance requirements • Strong communication, analytical, problem-solving, and technical documentation skills • Preferred: DHS Public Trust or higher clearance • Preferred: relevant cloud or AI/ML certification • Preferred: experience with LLMs, RAG, and generative AI integration • Preferred: familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines • Preferred: Docker and Kubernetes experience • Preferred: knowledge of federal data governance frameworks, policies, and tools • Preferred: Infrastructure as Code experience with Terraform, Ansible, CloudFormation, or equivalent • Preferred: additional cloud certifications across multiple providers • Preferred: Agile certification or demonstrated Agile experience

🏖️ Benefits

• Employees may be eligible for overtime • Shift differential may be available • Discretionary bonus may be available