3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Architect, build, automate, monitor, and optimize AWS AI/ML infrastructure for reliable, scalable, and cost-effective workloads.
- Write, review, and debug code, scripts, and infrastructure-as-code for AWS infrastructure, automation, monitoring, and deployment tooling.
- Configure GPU-enabled AWS compute and supporting storage and networking services, including EC2, EKS, and SageMaker.
- Support deployment automation and CI/CD pipelines for AI systems, models, and applications using Git, Terraform or CloudFormation, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate AI services, model-serving components, and data pipelines while applying reliability, security, scalability, and cost-efficiency practices.
- Monitor infrastructure and model-serving health with CloudWatch and related observability tools, troubleshooting compute, storage, networking, container, and application issues.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models into enterprise systems and meet compliance requirements.
- Document reusable infrastructure patterns, configuration standards, and operational runbooks.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field, with 15 years of full-time education.
- Minimum five years of experience is required; the qualifications also specify at least two years working with AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least two years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and the compute, storage, networking, security, and deployment foundations needed to run AI workloads.
- Hands-on experience with AWS AI infrastructure services including EC2, EKS, SageMaker, S3, IAM, VPC, and CloudWatch.
- Experience with GPU or accelerated compute, distributed training, containerized deployments, model serving, infrastructure-as-code, Kubernetes, workflow orchestration, and operational monitoring.
- Knowledge of MLOps patterns including experiment tracking, model registries, deployment, monitoring, and rollback.
- Preferred qualifications include AWS certifications, experience in regulated or enterprise industries, familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, security controls, FinOps, incident management, and production support.
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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.