24 days ago
Gurgaon, IndiaStaff+
Responsibilities
- Own end-to-end architecture and design of optimized AWS compute infrastructure for large-scale AI/ML systems.
- Design and tune AWS GPU clusters, distributed training systems, container platforms, model-serving environments, networking, storage, and accelerator configurations.
- Evaluate architecture alternatives and lead assessments of existing and proposed environments, identifying risks, bottlenecks, gaps, and optimization opportunities.
- Define infrastructure roadmaps, capacity models, scaling strategies, cost forecasts, performance improvements, and architecture decision records.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, performance tracking, and cost tracking.
- Integrate AI/ML systems into enterprise environments while addressing interoperability, security, compliance, regulatory alignment, and client standards.
- Collaborate with clients, stakeholders, architects, and engineering teams to align infrastructure decisions with business outcomes.
- Set technical direction, lead engineering workstreams, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required, along with 15 years of full-time education.
- A minimum of 12 years of experience is stated for the project role; the qualifications also specify at least 4 years of experience with AI/ML infrastructure engineering or related large-scale platform work.
- At least 4 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
- At least 4 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Experience with workflow and data pipeline tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Hands-on experience with AWS AI infrastructure services, including EC2, EKS, SageMaker, S3, FSx/EFS, IAM, VPC, CloudWatch, and AWS DevOps/security services.
- Experience architecting GPU or accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Working knowledge of Terraform or CloudFormation, CI/CD, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response practices.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and cloud services, and managing priorities across multiple initiatives.
- Preferred qualifications include AWS Solutions Architect Professional, AWS DevOps Engineer Professional, or AWS Machine Learning/AI certifications; experience in regulated industries; and exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, model optimization, FinOps, and enterprise architecture governance.
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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.