1 day ago
Bengaluru, IndiaStaff+
Responsibilities
- Own end-to-end architecture and design of optimized GCP compute infrastructure for large-scale AI/ML systems.
- Design and tune accelerated compute clusters, distributed training systems, model-serving environments, container platforms, networking, and high-throughput storage.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, observability, security, compliance, cost, and operational complexity.
- Lead architecture assessments, reviews, decision records, roadmap inputs, capacity planning, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, compliance, regulatory, and client standards.
- Collaborate with clients, stakeholders, architects, 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
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- Minimum 12 years of experience is required.
- At least four years of experience building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
- At least four years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- Strong understanding of AI/ML concepts and production AI workload infrastructure.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- At least four years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform.
- Experience leading AI projects or engineering workstreams and managing multiple initiatives.
- Experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- Hands-on experience with GCP AI infrastructure services, accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Working knowledge of Terraform, CI/CD, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Preferred qualifications include GCP certifications, regulated-industry experience, LLM infrastructure, vector databases, retrieval pipelines, accelerator scheduling, high-performance storage, low-latency model serving, model optimization, enterprise architecture governance, FinOps, and vendor collaboration.
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.