Data Platform Architect

About Accenture

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology or a related engineering field.
• Minimum 2 years of experience coding, building, monitoring or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines or cloud/platform engineering solutions.
• Strong understanding of AI/ML concepts and the compute, storage, networking, security and deployment foundations required to run AI workloads.
• Minimum 2 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash or PowerShell.
• Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration and operational monitoring tools.
• Strong problem-solving ability, communication skills and collaboration mindset in a fast-paced engineering environment.
• Required Skills/ Experience
• Hands-on experience with AWS services relevant to AI infrastructure such as EC2, EKS, SageMaker, S3, IAM, VPC, CloudWatch and related DevOps services.
• Experience designing or operating GPU/accelerated compute, distributed training setups, containerized deployments and model-serving workloads.
• Working knowledge of Terraform or CloudFormation, Docker, Kubernetes, CI/CD pipelines and observability practices.
• Ability to optimize infrastructure for performance, reliability, scalability, cost and security.
• Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring and rollback approaches.
• Good to Have Skills
• AWS certification such as AWS Certified Solutions Architect, Developer, DevOps Engineer or Machine Learning specialty/associate level.
• Exposure to industry use cases in BFSI, healthcare, retail/e-commerce, telecom, manufacturing or public sector where AI infrastructure must meet compliance, reliability and data-governance expectations.
• Familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling or model optimization techniques.
• Knowledge of security controls, FinOps practices, incident management and production support processes for enterprise AI platforms.
• 15 years full time education

Responsibilities

• Write, review and debug code, scripts and infrastructure-as-code for AWS AI infrastructure, automation, monitoring and deployment tooling.
• Configure and provision AWS compute resources for AI/ML workloads, including GPU-enabled instances, Amazon EC2, Amazon EKS, Amazon SageMaker and supporting storage/networking services.
• Support deployment automation and CI/CD pipelines for AI systems, models and applications using tools such as Git, Terraform/CloudFormation, Docker, Kubernetes and workflow orchestration tooling.
• Deploy and operate AI services, model-serving components and data pipelines while applying reliability, security, cost-efficiency and scalability practices.
• Monitor infrastructure and model-serving health using AWS CloudWatch and related observability tools troubleshoot issues across compute, storage, networking, containers and application layers.
• Collaborate with data scientists, ML engineers, platform engineers and architects to integrate AI models into enterprise systems while meeting compliance and operational requirements.
• Document reusable patterns, configuration standards and runbooks for AWS-based AI infrastructure.