Senior Ai Cloud Engineer

  • Washington D.C., DC
  • Posted 14 hours ago | Updated 14 hours ago

Overview

On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

Amazon DynamoDB
Communication
Computer Science
Continuous Delivery
Continuous Integration
Analytical Skill
Artificial Intelligence
Cloud Computing
Collaboration
Amazon Redshift
Amazon S3
Engineering Design
FOCUS
FedRAMP
Data Integration
Data Security
Deep Learning
Management
Mentorship
Kubernetes
Large Language Models (LLMs)
Leadership
Machine Learning (ML)
Docker
FISMA
Generative Artificial Intelligence (AI)
GitLab
IaaS
Amazon SageMaker
Amazon Web Services
Programming Languages
PyTorch
Python
Analytics
Data Engineering
DevSecOps
Terraform
Training
Transformer
Java
Workflow
NIST SP 800 Series
Privacy
Real-time
Regulatory Compliance
Scalability

Job Details

Design and build next-generation AI & analytics frameworks using AWS AI services (e.g., Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe).

Apply Generative AI models (e.g., GPT, Claude, LLaMA, HuggingFace) to solve complex business challenges across diverse domains.

Architect and deploy secure, scalable cloud-based data and AI platforms, including data lakes, warehouses, and real-time processing layers.

Lead cross-functional teams in delivering AI/ML pipelines, cloud infrastructure, and data integration solutions using tools like Terraform, Docker, Kubernetes, and GitLab.

Build and manage large-scale data pipelines using AWS data services (S3, Redshift, DynamoDB) ensuring performance, scalability, and governance.

Implement and enforce data security, privacy, and governance standards, with a focus on FedRAMP, FISMA, and other federal compliance frameworks.

Integrate AI model training and deployment into DevSecOps pipelines, automating end-to-end workflows using CI/CD tools.

Collaborate with stakeholders to convert business needs into technical solutions and actionable insights.

Mentor team members and conduct AI/cloud bootcamps and cross-training workshops to promote adoption and build internal capabilities.

Drive end-to-end ownership of AI-powered data products and cloud-native services.

Qualifications:

10+ years of hands-on experience in AI engineering, cloud infrastructure, and data product delivery.

Bachelor's or Master's degree in Computer Science, AI, ML, Data Engineering, or equivalent work experience.

Expertise in Generative AI, deep learning, and large language models, including Transformer-based architectures (PyTorch, HuggingFace, etc.).

Proficiency in Python, Java, or other relevant programming languages.

Experience designing and operating solutions in AWS, including both AI and data services.

Strong understanding of DevSecOps, CI/CD practices, and infrastructure-as-code (IaC).

Knowledge of government cloud standards, including FedRAMP, FISMA, and NIST SP 800-53 controls.

Excellent leadership, analytical, and communication skills to drive high-impact technical initiatives across multiple teams.

Required Certifications (at least one):

AWS Certified Machine Learning Specialty

AWS Certified Solutions Architect Professional

Other relevant AI or cloud certifications will be considered

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.