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Accenture

Advises businesses and runs their technology

Data Platform Architect

Cloud EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related Technical Architecting field is required.
  • At least 18 years of experience is required.
  • At least 6 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale Technical Architecting solutions.
  • A strong understanding of AI/ML concepts and the compute, infrastructure, orchestration, and deployment foundations needed to run production AI systems is required.
  • At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent Technical Architecting languages is required.
  • Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling is required.
  • Experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance, and stakeholder alignment, is required.
  • Expert-level hands-on architecture experience with AWS AI infrastructure services including EC2, EKS, SageMaker, S3, IAM, VPC, CloudWatch, and automation/DevOps services is required.
  • Deep knowledge of GPU/accelerated compute, distributed training, model serving, container platforms, storage design, network design, observability, and resilience Technical Architecting is required.
  • Strong experience with Terraform/CloudFormation, CI/CD, Docker, Kubernetes, security guardrails, monitoring, and infrastructure cost optimization is required.
  • The ability to evaluate multiple AWS architecture options and produce standards, patterns, decision records, benchmarks, and executive-ready recommendations is required.
  • Experience applying MLOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response, and rollback strategies is required.

Responsibilities

  • Set the overarching AWS AI infrastructure vision, strategy, and reference architecture for large-scale AI/ML systems, including compute, networking, storage, orchestration, model serving, and observability.
  • Own complex architectural decisions across AWS services such as EC2, EKS, SageMaker, S3, FSx/EFS, VPC, IAM, CloudWatch, and related DevOps/security tooling, rationalizing options against client standards and business objectives.
  • Architect and prototype cost-optimized GPU/accelerated compute and distributed training environments, building benchmarks, proofs of concept, and reusable implementation patterns.
  • Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, model deployment patterns, monitoring strategy, SLAs/SLOs, and cost/performance governance for production AI/ML systems.
  • Lead architecture assessments and design reviews, validate findings through hands-on implementation, profiling, performance tuning, and troubleshooting across the AI infrastructure stack.
  • Evaluate emerging AI infrastructure technologies, accelerators, interconnects, managed AI services, and ecosystem tools, and recommend where they belong in enterprise solutions.
  • Provide executive- and client-level technical advisory, translating cloud infrastructure trade-offs into clear, defensible recommendations connected to business outcomes.
  • Mentor architects and Technical Architects, build community best practices, and represent the practice in internal and external technical forums.

Desired Qualifications

  • AWS certifications such as Solutions Architect Professional, DevOps Technical Architect Professional, or Machine Learning specialty/associate credentials.
  • Industry experience designing AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy, or public sector environments with compliance, security, and reliability constraints.
  • Exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, high-performance storage, and low-latency model serving.
  • Experience with enterprise architecture governance, technology roadmaps, vendor/partner management, FinOps, and production support operating models.
  • AI Agents & Workflow Integration.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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