3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own the 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, container platforms, model-serving environments, networking, and high-throughput storage.
- Evaluate architecture alternatives and lead assessments, reviews, remediation planning, and architecture decision-making.
- Define infrastructure roadmaps, capacity models, scaling strategies, cost forecasts, and performance improvements.
- Design deployment automation and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish AI 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 addressing interoperability, security, compliance, and regulatory requirements.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business needs into architecture standards.
- Set technical direction, mentor engineers, review designs and code, and promote engineering best practices.
Requirements
- Minimum 12 years of experience is required.
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 4 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 4 years of proficiency with programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- At least 4 years of AI/ML infrastructure engineering or related experience on a hyperscaler or enterprise platform.
- Experience with data pipeline and workflow tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong knowledge of AI/ML concepts, production AI workloads, GCP AI infrastructure services, accelerated compute, distributed training, model serving, storage, containers, and secure cloud networking.
- Strong working knowledge of Terraform, CI/CD, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response practices.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing multiple initiatives.
- 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, model optimization, enterprise architecture governance, FinOps, and infrastructure 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.