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Role Description Job Title: Cloud AI/ML Engineer – Generative AI (AWS) About The Role We are seeking a skilled and forward-thinking Cloud AI/ML Engineer to lead the design, development, and support of scalable, secure, and high-performance generative AI applications on AWS . You’ll operate at the crossroads of cloud engineering and artificial intelligence, enabling rapid and reliable delivery of cutting-edge AI solutions using services like Amazon Bedrock and SageMaker . This is an opportunity to join a collaborative team driving innovation in AI infrastructure, with a strong focus on automation, security, observability, and performance optimization. Roles And Responsibilities AI/ML Integration Utilize Amazon Bedrock for leveraging foundation models and Amazon SageMaker for training and deploying custom models. Design and maintain scalable generative AI applications using AWS-native AI/ML tools and services. Deployment & Operations Build and manage CI/CD pipelines to automate infrastructure provisioning and model lifecycle workflows. Monitor infrastructure and model performance using Amazon CloudWatch and other observability tools. Ensure production-grade availability, fault tolerance, and performance of deployed AI systems. Security & Compliance Enforce security best practices using IAM, data encryption, and access control policies. Maintain compliance with relevant organizational, legal, and industry-specific data protection standards. Collaboration & Support Partner with data scientists, ML engineers, and product teams to translate requirements into resilient cloud-native solutions. Diagnose and resolve issues related to model behavior, infrastructure health, and AWS service usage. Optimization & Documentation Continuously assess and optimize model performance, infrastructure cost, and resource utilization. Document deployment workflows, architectural decisions, and operational runbooks for team-wide reference. Mentorship & Guidance Mentor peers and junior engineers by sharing knowledge of AWS services and generative AI best practices. Must-Have Skills & Experience Expertise in AWS services, particularly SageMaker, Bedrock, EC2, IAM, and related cloud-native tools. Strong coding skills in Python, with experience in developing AI applications. Hands-on experience with Docker for containerization and familiarity with Kubernetes for orchestration. Proven experience building and maintaining CI/CD pipelines for AI/ML workloads. Knowledge of data security, access control, and monitoring within cloud environments. Experience managing cloud-based data flows and infrastructure for ML workflows. Good-to-Have (Preferred) Skills AWS certifications, such as: AWS Certified Machine Learning – Specialty AWS Certified DevOps Engineer Understanding of responsible AI practices, particularly in generative model deployment. Experience in cost optimization, auto-scaling, and resource management for production AI workloads. Familiarity with tools like Terraform, CloudFormation, or Pulumi for infrastructure as code (IaC). Exposure to multi-cloud or hybrid cloud strategies involving AI/ML services. Skills Aws,Python,Docker,Kubernets Show more Show less
