Cloud AI Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop, train, and implement machine learning models utilizing managed AI/ML services across AWS, Azure, GCP, and OCI.

• Create and sustain ML pipelines for data ingestion, feature engineering, model training, evaluation, and deployment.

• Establish model-serving infrastructure, which includes real-time inference endpoints, batch prediction workflows, and API integrations.

• Embed large language models and generative AI features into government applications while ensuring compliance and safeguards.

• Design and execute cloud-native ETL, data lake, and feature-store workflows.

• Assemble structured and unstructured training datasets, ensuring data quality, lineage, and governance are maintained.

• Enhance data pipelines for optimal performance, cost-effectiveness, and reliability across cloud platforms.

• Oversee deployed models to identify performance degradation, data drift, and bias.

• Diagnose AI/ML workload challenges, including training failures, inference latency, and resource utilization issues.

• Maximize cloud resource efficiency and costs, including GPU/accelerator allocation and the use of spot/preemptible instances.

• Collaborate with data scientists, application developers, and infrastructure engineers to implement AI/ML solutions.

• Document AI/ML architectural decisions, deployment protocols, and operational runbooks.

• Assist with service delivery metrics and reporting in coordination with the Service Delivery Manager and ISR Product Owner.

• Adhere to Change Management procedures for production AI/ML deployments.

⛳️ Requirements

• Bachelor's degree with 5 years of experience, or an Associate's degree with 7 years of experience, or a high school diploma/equivalent with 9 years of experience.

• Must be a U.S. Citizen.

• Capability to obtain and maintain a DHS Public Trust.

• 3 to 5 years of experience in AI/ML engineering, data engineering, or applied machine learning with cloud-based technologies.

• Practical experience with managed AI/ML services on at least two cloud platforms: AWS, Azure, GCP, or OCI.

• Proficient in Python and familiar with TensorFlow, PyTorch, scikit-learn, or similar technologies.

• Experience in designing, building, deploying, and managing ML pipelines and model-serving infrastructure in production cloud environments.

• Familiarity with cloud-based AI services, including generative AI, large language models, machine learning platforms, or related AI capabilities.

• Knowledge of responsible AI, model governance, data governance, and federal compliance standards.

• Excellent communication, analytical, problem-solving, and technical documentation abilities.

• Preferred: DHS Public Trust or a higher level of clearance.

• Preferred: relevant cloud or AI/ML certification.

• Preferred: experience with LLMs, RAG, and generative AI integration.

• Preferred: understanding of MLOps tools such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines.

• Preferred: experience with Docker and Kubernetes.

• Preferred: knowledge of federal data governance frameworks, policies, and tools.

• Preferred: Infrastructure as Code experience with Terraform, Ansible, CloudFormation, or similar tools.

• Preferred: additional cloud certifications across multiple providers.

• Preferred: Agile certification or demonstrated experience in Agile methodologies.

🏝️ Benefits

• Employees may qualify for overtime compensation.

• Shift differentials may be available.

• Discretionary bonuses may be offered.