24 days ago
Gurgaon, IndiaStaff+
Responsibilities
- Architect and build optimized AWS compute infrastructure for large-scale AI/ML systems, including distributed training, GPU-accelerated compute, container platforms, and model-serving environments.
- Design and tune AWS GPU clusters using EC2, EKS, SageMaker, S3, FSx/EFS, VPC, IAM, and CloudWatch.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments, reviews, remediation planning, and architecture decision documentation.
- Define infrastructure roadmaps, capacity planning models, scaling strategies, cost forecasts, and performance improvements.
- Design deployment, automation, and release strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while maintaining interoperability, security, compliance, and regulatory alignment.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, 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.
- A minimum of 12 years of experience is stated for the project role; the qualifications also specify at least 4 years in AI/ML infrastructure engineering or related hyperscaler or enterprise-platform roles.
- 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.
- Strong understanding of AI/ML concepts and infrastructure for deploying and optimizing production AI workloads.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Hands-on experience with AWS AI infrastructure services, GPU or accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Working knowledge of Terraform or CloudFormation, Docker, Kubernetes, observability, incident response, InfraOps, and MLOps practices.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and cloud services, and managing priorities across multiple initiatives.
- Strong problem-solving, communication, collaboration, and stakeholder-alignment skills.
- Preferred qualifications include AWS Solutions Architect Professional, AWS DevOps Engineer Professional, or AWS Machine Learning/AI certifications; regulated-industry experience; 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.