3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Architect, build, automate, monitor, and optimize AI/ML infrastructure on Google Cloud Platform.
- Write, review, and debug code, scripts, and infrastructure-as-code for AI 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 pipelines for AI systems, models, and applications using Git, Terraform, Cloud Build, Docker, Kubernetes, and workflow orchestration tools.
- Deploy and operate AI services, model-serving components, and data pipelines while optimizing reliability, security, cost, and scalability.
- Monitor infrastructure and model-serving health with Cloud Monitoring, Cloud Logging, and related observability tools, and troubleshoot compute, storage, networking, container, and application issues.
- Collaborate with data scientists, ML engineers, platform engineers, and architects to integrate AI models into enterprise systems.
- Document reusable infrastructure patterns, configuration standards, and runbooks for GCP-based AI platforms.
Requirements
- At least five years of experience is required.
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least two years of experience coding, building, monitoring, or troubleshooting AI/ML infrastructure, data platforms, model deployment pipelines, or cloud/platform engineering solutions.
- At least two 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 required for AI workloads.
- Experience with infrastructure as code, containers, Kubernetes, workflow orchestration, CI/CD pipelines, and operational monitoring tools.
- Hands-on experience with GCP services including Compute Engine, GKE, Vertex AI, Cloud Storage, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging.
- Experience designing or operating accelerated compute, distributed training, containerized deployments, and model-serving workloads.
- Understanding of MLOps patterns including experiment tracking, model registry, model deployment, monitoring, and rollback.
- Preferred qualifications include a GCP certification, familiarity with large language model infrastructure, vector databases, retrieval pipelines, GPU scheduling, model optimization, security controls, FinOps, incident management, and production support.
Tech Stack
Categories
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.