3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own end-to-end architecture and design of GCP compute infrastructure for large-scale AI and ML systems.
- Design and tune accelerated compute clusters, distributed training systems, model-serving environments, container platforms, networking, and high-throughput storage.
- Evaluate architecture alternatives and document decisions, trade-offs, risks, dependencies, standards, and remediation plans.
- Define infrastructure roadmaps, capacity plans, scaling strategies, cost forecasts, and performance-improvement opportunities.
- Design reliable deployment, automation, and CI/CD strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, and performance and cost tracking.
- Integrate AI and ML systems into enterprise environments while meeting security, compliance, regulatory, interoperability, and client-standard requirements.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into actionable infrastructure standards.
- Set technical direction for workstreams, mentor engineers, and review designs and code.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 12 years of experience is stated for the project role; the qualifications also specify at least 4 years of AI/ML infrastructure or related engineering experience.
- At least 4 years of experience coding, 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.
- Experience with AI/ML concepts, production AI workloads, data pipelines, workflow-management tools such as Apache Airflow or Kubeflow, and large-scale cloud or enterprise platforms.
- 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 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.
- Strong problem-solving, communication, collaboration, stakeholder-alignment, and client-delivery skills.
- Preferred qualifications include GCP Professional Cloud Architect, Professional Data Engineer, Professional Machine Learning Engineer, or Professional Cloud DevOps Engineer certifications.
- Preferred experience includes regulated-industry AI infrastructure, 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.