AI Automations Cloud Deployment Engineer (Remote - US)

This position is posted by Jobgether on behalf of a partner company. We are currently looking for an AI Automations Cloud Deployment Engineer in the United States.

As an AI Automations Cloud Deployment Engineer, you will lead the end-to-end design, deployment, and optimization of AI and machine learning solutions across major cloud platforms. You will build scalable, secure infrastructures that power intelligent systems and advanced analytics within a global organization. Working closely with cross-functional teams, you will ensure AI models are seamlessly deployed and maintained for real-world performance and impact. This position offers the opportunity to work with cutting-edge cloud and AI technologies, contributing to large-scale innovation in a highly technical and mission-driven environment.

Accountabilities:

• Design, implement, and manage cloud-based infrastructures to support AI and machine learning workloads on platforms such as AWS, Azure, or GCP.
• Develop and optimize MLOps pipelines for model training, testing, deployment, and continuous monitoring.
• Apply best practices in cloud architecture to achieve scalability, reliability, and cost efficiency while maintaining strong security and compliance standards.
• Collaborate with data scientists, engineers, and architecture teams to deploy AI models and integrate them within enterprise systems.
• Use containerization (Docker) and orchestration (Kubernetes) technologies to streamline deployment and scaling of AI applications.
• Explore and integrate emerging technologies such as generative AI, LLMs, and retrieval-augmented generation (RAG) to enhance automation capabilities.
• Provide technical leadership, guidance, and stakeholder engagement throughout project delivery cycles.

• Bachelor’s degree in Computer Science, Engineering, or a related field, with 10+ years of experience in AI, ML, or cloud solution engineering.
• Strong background in MLOps tools, principles, and lifecycle management.
• Professional certification in AWS or Azure (AI/ML specialization preferred).
• Proficiency in Python, Java, or C++, and familiarity with Terraform or CloudFormation.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Exposure to generative and agentic AI frameworks.
• Strong communication, collaboration, and analytical problem-solving skills.
• Master’s degree or SAFe DevOps certification is a plus.
• Candidates must meet U.S. Person eligibility requirements.