16 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Own the architecture and implementation of AWS compute infrastructure for large-scale AI/ML systems, including distributed training, GPU compute, containers, and model serving.
- Design and tune AWS GPU clusters and distributed systems using EC2, EKS, SageMaker, S3, FSx/EFS, VPC, IAM, and CloudWatch.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, observability, security, compliance, cost, and operational complexity.
- Lead architecture reviews, identify risks and bottlenecks, and recommend remediation and optimization actions.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, and cost and performance tracking.
- Integrate AI/ML systems into enterprise environments while addressing interoperability, security, compliance, and regulatory requirements.
- Set technical direction, lead workstreams, review designs and code, and mentor engineers.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field, with 15 years of full-time education stated.
- At least 12 years of professional experience is required; the role also specifies at least 4 years in AI/ML infrastructure or related large-scale engineering work and at least 4 years of programming or scripting proficiency.
- Hands-on experience with AWS AI infrastructure services, including EC2, EKS, SageMaker, S3, FSx/EFS, IAM, VPC, CloudWatch, and AWS DevOps and security services.
- Experience architecting GPU or accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Experience with data pipeline and workflow tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Working knowledge of Terraform or CloudFormation, CI/CD, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Proven leadership of AI projects or engineering workstreams, technology evaluation, stakeholder alignment, and prioritization across multiple initiatives.
- Preferred qualifications include AWS certifications, experience in regulated industries, exposure to LLM infrastructure and vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, model optimization, enterprise architecture governance, FinOps, and vendor collaboration.
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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.