Data Engineer

Project Role : Data Engineer Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Must have skills : Machine Learning Operations Good to have skills : Amazon Web Services (AWS) Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent As a Senior Engineer in AI Infrastructure Architecture for AWS, you will own significant portions of the end-to-end architecture and engineering of optimized compute infrastructure for large-scale AI and machine learning systems. You will design scalable distributed training environments, model-serving foundations, automation patterns and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, cloud modernization, regulated workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust AWS-based AI infrastructure solutions. Key Responsibilities Own end-to-end architecture and design of optimized AWS compute infrastructure for large-scale AI/ML systems, including distributed training, GPU/accelerated compute, container platforms and model-serving environments. Design and tune large-scale AWS GPU clusters and distributed training systems using services such as EC2, EKS, SageMaker, S3, FSx/EFS, VPC, IAM and CloudWatch, including accelerator selection, interconnect/networking and high-throughput storage design. Serve as an authoritative AI infrastructure expert on AWS, applying deep knowledge of AWS AI/ML services, accelerators, networking, security and cost levers. Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity. Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions. Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards. Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities. Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.