3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Define the AWS AI infrastructure vision, strategy, reference architecture, standards, and implementation approach for large-scale AI/ML systems.
- Make architectural decisions across compute, networking, storage, orchestration, model serving, observability, security, resilience, and cost optimization.
- Architect and prototype GPU-accelerated compute and distributed training environments using benchmarks, proof-of-concepts, and reusable implementation patterns.
- Define infrastructure-as-code, CI/CD, model deployment, monitoring, SLA/SLO, MLOps, incident response, rollback, and cost/performance governance practices.
- Lead architecture assessments and design reviews, validating findings through hands-on implementation, profiling, performance tuning, and troubleshooting.
- Evaluate emerging AI infrastructure technologies, accelerators, managed AI services, frameworks, and ecosystem tools for enterprise use.
- Provide executive- and client-level technical advisory and translate infrastructure trade-offs into business recommendations.
- Lead AI infrastructure projects and teams, mentor architects, build community best practices, and represent the practice in technical forums.
Requirements
- A bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology, or a related technical field is required.
- A minimum of 18 years of experience is stated for the project role.
- 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 architecture solutions.
- At least 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and the infrastructure, orchestration, compute, and deployment foundations needed for production AI systems.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Expert hands-on architecture experience with AWS AI infrastructure services including EC2, EKS, SageMaker, S3, IAM, VPC, CloudWatch, and DevOps automation services.
- Strong experience with GPU-accelerated compute, distributed training, model serving, container platforms, storage design, network design, observability, resilience, Terraform or CloudFormation, Docker, Kubernetes, security guardrails, monitoring, and infrastructure cost optimization.
- Experience applying MLOps or InfraOps practices for experiment tracking, model registries, deployment automation, monitoring, incident response, and rollback strategies.
- Proven experience leading AI infrastructure projects and teams, conducting design reviews, managing delivery governance, aligning stakeholders, and evaluating production AI technologies and architectures.
- Preferred qualifications include AWS Solutions Architect Professional, AWS DevOps Engineer Professional, or AWS Machine Learning certifications; industry experience in regulated sectors; exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, high-performance storage, low-latency model serving, enterprise architecture governance, roadmaps, vendor management, FinOps, and production support models.
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About Accenture
Accenture is a global professional services firm providing management consulting, systems integration and technology, cybersecurity, and business process outsourcing for enterprises and governments. It operates a services-driven model delivering projects and managed services, often with major cloud and software partners, across industries worldwide. Headquartered in Dublin and publicly traded on the NYSE (ACN), it originated as Andersen Consulting and adopted the Accenture name in 2001.