3 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Own the architecture and design of optimized GCP compute infrastructure for large-scale AI and machine learning systems.
- Design accelerated compute clusters, distributed training environments, model-serving foundations, container platforms, networking, and high-throughput storage.
- Evaluate architecture alternatives and lead assessments, reviews, decision records, remediation planning, and infrastructure roadmaps.
- Design deployment, automation, and release 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, regulatory alignment, and client standards.
- 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
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 12 years of experience is required overall, including at least 4 years building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure or related large-scale engineering solutions.
- At least 4 years of programming or scripting experience with languages such as Python, Java, C++, Bash, or PowerShell is required.
- Hands-on experience with GCP AI infrastructure services, including Compute Engine, GKE, Vertex AI, Cloud Storage, Filestore, IAM, VPC, Cloud Build, Cloud Monitoring, and Cloud Logging.
- Experience with accelerated compute, distributed training, model serving, high-throughput storage, container platforms, secure cloud networking, Terraform, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response.
- Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Demonstrated experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing multiple initiatives.
- Strong communication, collaboration, stakeholder alignment, problem-solving, and client delivery skills.
- Preferred qualifications include relevant GCP certifications, regulated-industry experience, exposure to LLM infrastructure and vector databases, and knowledge of enterprise architecture governance, FinOps, vendor collaboration, and production support models.
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.