Effectual AI Cloud Engineers are members of the Public Sector Program Management team responsible for ensuring that customer-facing projects are delivered with exceptional customer satisfaction and technical excellence. The AI Cloud Engineer designs, deploys, modernizes, and optimizes AI-enabled cloud environments. This role requires deep expertise in AI, cloud architecture, and AI-driven workloads in AWS environments, with demonstrated experience accelerating legacy-to-cloud migrations and advising leadership on AI strategy and vision. The position requires hands-on engineering capability, architectural oversight, and the ability to assess current-state environments and implement secure, scalable, compliant AI cloud solutions. Effectual AI Cloud Engineers are “Brand Ambassadors” and are expected to stay current on leading practices to deliver high-quality, well-conceived solutions to customers.
A Glimpse into the Daily Routine of an AI Cloud EngineerArchitect and refine AI-driven cloud solutions within AWS, deploy and optimize workloads through managed AWS AI services and containerized platforms. Modernize legacy applications, accelerate secure cloud-native migrations, and evaluate existing infrastructure to recommend AI-enabled enhancements aligned with mission objectives. Responsibilities include but not limited to implement Infrastructure as Code and CI/CD automation, integrate AI capabilities into enterprise systems and data platforms, leverage tools such as Kiro (formerly Amazon Q Developer) to enhance development efficiency, participate in architecture and security reviews, and advise technical leadership on AI strategy, roadmap execution, and cloud optimization initiatives.
Essential Duties and Responsibilities- Provide technical leadership in AI adoption strategy and recommend scalable AI solutions aligned with mission requirements
- Design and implement scalable, secure AWS AI cloud architectures and solutions
- Lead migration of legacy systems to AWS, including re-architecture, refactoring, containerization, and modernization efforts
- Deploy AI solutions using AWS services such as SageMaker, Bedrock, Kiro, Lambda, ECS, EKS, and related services
- Utilize AI-assisted engineering tools including Kiro or equivalent platforms to improve development velocity and solution quality
- Conduct performance tuning and cost optimization for AI cloud workloads
- Develop and manage Infrastructure as Code using tools like CloudFormation or Terraform
- Engineer CI/CD pipelines for AI model deployment and cloud infrastructure automation
- Implement MLOps practices including model versioning, monitoring, retraining workflows, and performance optimization
- Ensure compliance with security standards including but not limited to FedRAMP and NIST
- Assess current environments, identify architectural gaps, and implement corrective improvements
- Collaborate with cross-functional teams, at a minimum, cybersecurity, DevSecOps, and program leadership teams to maintain secure and compliant cloud ecosystems
- Provide architecture documentation, technical briefings, and implementation guidance to leadership
- Stay updated with emerging AI cloud technologies, industry trends, and best practices
- Minimum Education:Bachelor’s degree in related discipline. Certifications: AWS Professional Certification(s) and AWS Certified Machine Learning - Specialty AND
- Minimum Experience: Must have a minimum of 8 years of experience in specialized AI cloud technologies including 4 years specializing in AWS AI cloud technologies OR
- Substitution/Alternative to Minimum Education and Experience: 10 years of on-the-job experience in lieu of Bachelor’s Degree
- Must be a US Citizen
- Proven experience to architect AI solutions in secure, compliance-driven ecosystems
- Deep expertise in AWS AI services including model development, deployment, and integration
- Strong experience in cloud-native architecture, automation, and DevSecOps practices
- Proven experience modernizing legacy systems and implementing cloud transformation strategies
- Experience designing scalable AI pipelines using managed cloud services
- Strong understanding of federal security compliance frameworks and cloud governance
- Experience performing cloud cost modeling and AI workload financial optimization
- Strong problem-solving and troubleshooting skills
- Excellent communication and collaboration skills to work with cross-functional teams
- Ability to work with multiple clients, in parallel
- Ability to work Eastern Standard Time Zone schedule
- Active Clearance or Public Trust (DOJ Preferred)
- Familiarity with generative AI architectures and large language model deployment
- Experience with container orchestration platforms including Kubernetes and EKS
- Strong background in DevSecOps practices within regulated government programs
- Experience integrating AI into enterprise ERP, data lake, or mission systems
- Knowledge of Zero Trust architectures within cloud environments
- Experience advising executive leadership on AI adoption strategy and digital transformation initiatives
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