Cloud AI Engineer

Peraton•,

5d•$104,000 - $166,000•Remote

About The Position

Peraton is seeking a Mid-Level Cloud AI Engineer to support the development, deployment, and operation of artificial intelligence and machine learning solutions across a multi-cloud government environment serving 70+ customer tenants and growing. The environment spans AWS, Microsoft Azure, Google Cloud Platform (GCP), and Oracle Cloud Infrastructure (OCI).

Requirements

Bachelors degree and 5 years of experience or an Associates degree and 7 years of experience or a High School diploma/equivalent and 9 years of experience.
Must be a U.S. Citizen with the ability to obtain/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 of the following cloud platforms: AWS, Azure, Google Cloud Platform (GCP), or Oracle Cloud Infrastructure (OCI).
Proficiency in Python and experience with machine learning frameworks and libraries such as 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 practices, model governance, data governance, and compliance requirements associated with deploying AI solutions in federal government environments.
Strong communication, analytical, problem solving, and technical documentation skills.

Nice To Haves

DHS Public Trust or higher clearance
Relevant cloud or AI/ML certification, such as AWS Machine Learning Specialty, Azure AI Engineer Associate, Google Professional Machine Learning Engineer, OCI AI Foundations Associate, or an equivalent certification.
Experience working with large language models, Retrieval Augmented Generation (RAG), and the integration of generative AI services.
Familiarity with MLOps practices, processes, and tools such as MLflow, Kubeflow, SageMaker Pipelines, Azure ML Pipelines, or equivalent technologies.
Experience using containerization technologies, including Docker and Kubernetes, to support AI/ML workloads.
Knowledge of data governance frameworks, policies, and tools applicable to federal data environments and data handling requirements.
Experience with Infrastructure as Code (IaC) tools such as Terraform, Ansible, CloudFormation, or equivalent technologies.
Additional cloud certifications across multiple cloud service providers.
Relevant Agile certification or demonstrated experience working in Agile development environments.

Responsibilities

Build, train, and deploy machine learning models using managed AI/ML services across AWS (SageMaker, Bedrock), Azure (Azure ML, Azure OpenAI Service), GCP (Vertex AI), and OCI (OCI Data Science, OCI Generative AI)
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 integration patterns
Support the integration of large language models and generative AI capabilities into government applications with appropriate guardrails and compliance controls
Design and implement data processing workflows using cloud-native services for ETL, data lake management, and feature stores
Work with structured and unstructured data sources to prepare training datasets, ensuring data quality, lineage, and governance requirements are met
Optimize data pipelines for performance, cost, and reliability across cloud platforms
Monitor deployed models for performance degradation, data drift, and bias using platform-native and third-party monitoring tools
Troubleshoot and resolve issues across AI/ML workloads, including training failures, inference latency, and resource utilization problems
Optimize cloud resource usage and costs for AI/ML workloads including GPU/accelerator allocation and spot/preemptible instance strategies
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 in coordination with the Service Delivery Manager and ISR Product Owner
Adhere to Change Management procedures for all production AI/ML deployments