AI/ML Cloud Computing Application Architect

Booz Allen Hamilton•Ashburn, VA

About The Position

AI/ML Cloud Computing Application Architect The Opportunity: Everyone is trying to “harness the cloud,” but not everyone knows how. As a cloud computing application architect, you know how to create a cloud-based technical architecture that meets client needs and takes advantage of cloud capabilities. You’ll recommend tools and solutions based on your research of the current environment and knowledge of various on-premise, cloud-based, and hybrid resources. You’ll use your experience to guide your team as they help the client overcome their most difficult challenges in the cloud. We are seeking a Senior AI/ML Architect with 10+ years of experience to lead the design and integration of AI/ML solutions within an enterprise cloud platform supporting AWS, Google Cloud, and Azure. This role requires expertise in cloud-native AI/ML services, especially on AWS and Google Cloud, and the ability to architect solutions that both enhance cloud platform operations and deliver AI/ML capabilities to customers. What You'll Work On: Architect AI/ML solutions for cloud platform operations. Apply AI/ML to optimize provisioning, scaling, monitoring, cost management, and security across multi-cloud environments. Design customer-facing AI/ML services. Build and manage offerings such as predictive analytics, NLP, computer vision, and generative AI using cloud-native services. Develop reference architectures and patterns, including for AI/ML workloads across AWS, Google Cloud, and Azure, ensuring scalability, resilience, and compliance. Integrate cloud-native AI/ML services including AWS such as SageMaker, Comprehend, Rekognition, Forecast, or Bedrock, Google Cloud, such as Vertex AI, AutoML, BigQuery ML, or Generative AI Studio, and Azure, such as Azure Machine Learning, or Cognitive Services. Enable MLOps and automation. Standardize CI/CD for ML pipelines, implement feature stores, model registries, and automated retraining workflows. Design data ingestion, transformation, and storage strategies for structured and unstructured data, leverage data lakes and warehouses. Join us. The world can’t wait.

Requirements

10+ years of experience in solution architecture
5+ years of experience with AI/ML systems
Experience in AWS and Google Cloud AI/ML services
Experience with MLOps, containerization, including Kubernetes, and IaC, including Terraform or CloudFormation
Experience with Python, TensorFlow, PyTorch, and ML pipeline orchestration tools
Knowledge of data engineering for AI/ML, including ETL, streaming, and big data frameworks such as Spark or Flink
Knowledge of Azure ML
Ability to design secure, compliant AI/ML solutions for enterprise environments
Ability to obtain and maintain a Public Trust or Suitability/Fitness determination based on client requirements
Bachelor’s degree in Computer Science or Engineering

Nice To Haves

Experience with Databricks for large-scale data and ML workflows
Experience with generative AI and LLM integration using cloud-native services
Knowledge of responsible AI frameworks and bias mitigation strategies
Master’s degree in Computer Science, Engineering, or a related field
AWS Machine Learning, Google Professional ML Engineer, or Azure AI Engineer Associate Certification

Responsibilities

Architect AI/ML solutions for cloud platform operations.
Apply AI/ML to optimize provisioning, scaling, monitoring, cost management, and security across multi-cloud environments.
Design customer-facing AI/ML services.
Build and manage offerings such as predictive analytics, NLP, computer vision, and generative AI using cloud-native services.
Develop reference architectures and patterns, including for AI/ML workloads across AWS, Google Cloud, and Azure, ensuring scalability, resilience, and compliance.
Integrate cloud-native AI/ML services including AWS such as SageMaker, Comprehend, Rekognition, Forecast, or Bedrock, Google Cloud, such as Vertex AI, AutoML, BigQuery ML, or Generative AI Studio, and Azure, such as Azure Machine Learning, or Cognitive Services.
Enable MLOps and automation.
Standardize CI/CD for ML pipelines, implement feature stores, model registries, and automated retraining workflows.
Design data ingestion, transformation, and storage strategies for structured and unstructured data, leverage data lakes and warehouses.