3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Own the architecture and design of optimized GCP compute infrastructure for large-scale AI and machine learning systems.
- Design and tune accelerated compute clusters, distributed training systems, container platforms, model-serving environments, networking, and high-throughput storage.
- Evaluate architecture alternatives and optimize infrastructure for performance, power consumption, cost, scalability, security, reliability, and compliance.
- Lead architecture assessments and reviews, identify risks and bottlenecks, and recommend remediation and optimization actions.
- Define infrastructure roadmaps, capacity planning models, scaling strategies, cost forecasts, and performance improvements.
- Design deployment, automation, and release strategies for AI systems, models, data pipelines, and platform components.
- Establish monitoring and observability practices across infrastructure and MLOps, including SLAs, SLOs, alerting, and cost and performance tracking.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, 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 lead AI projects or engineering workstreams.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 12 years of experience is stated in the project requirements, with additional requirements specifying 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 in programming or scripting languages such as Python, Java, C++, Bash, or PowerShell.
- 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, data pipelines, and workflow management tools such as Apache Airflow or Kubeflow.
- Strong working knowledge of Terraform, Docker, Kubernetes, infrastructure operations, MLOps, observability, and incident response practices.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Experience leading AI projects or engineering workstreams, evaluating AI technologies and cloud services, and managing priorities across multiple initiatives.
- Preferred qualifications include relevant GCP certifications, regulated-industry experience, exposure to LLM infrastructure and vector databases, knowledge of retrieval pipelines and accelerator scheduling, and familiarity with FinOps and enterprise architecture governance.
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.