16 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Design, build, automate, monitor, and optimize AI/ML infrastructure on Google Cloud Platform.
- Write, review, and debug code, scripts, and infrastructure-as-code for infrastructure automation, monitoring, and deployment tooling.
- Configure and provision Compute Engine, Google Kubernetes Engine, Vertex AI, Cloud Storage, and related networking and security services.
- Support deployment automation and CI/CD pipelines using Git, Terraform, Cloud Build, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate AI services, model-serving components, and data pipelines with attention to reliability, security, scalability, and cost efficiency.
- Monitor infrastructure and model-serving health with Cloud Monitoring, Cloud Logging, and related observability tools.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models into enterprise systems.
- 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.
- At least 5 years of experience is stated for the project role; the detailed qualifications also require at least 2 years of relevant AI/ML infrastructure, data platform, model deployment, or cloud/platform engineering experience.
- At least 2 years of proficiency in 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 for AI workloads.
- Experience with CI/CD, infrastructure-as-code, containers, Kubernetes, workflow orchestration, and operational monitoring tools.
- Hands-on experience with GCP services including Compute Engine, Google Kubernetes Engine, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging.
- Experience with accelerated compute, distributed training, containerized deployments, and model-serving workloads.
- Understanding of MLOps practices including experiment tracking, model registries, deployment, monitoring, and rollback.
- GCP certifications and familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, security controls, FinOps, incident management, and production support are preferred.
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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.