3 days ago
Bengaluru, IndiaSenior
Responsibilities
- Architect and build optimized GCP compute infrastructure for large-scale AI and machine learning systems.
- Design accelerated compute clusters, distributed training systems, container platforms, high-throughput storage, and model-serving environments.
- Evaluate architecture alternatives across compute, networking, storage, orchestration, observability, security, compliance, cost, and operational complexity.
- 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 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 cost and performance tracking.
- Integrate AI/ML systems into enterprise environments while meeting interoperability, security, compliance, regulatory, and client standards.
- Collaborate with clients, architects, stakeholders, and engineering teams to translate business requirements into infrastructure standards.
- Set technical direction, review designs and code, lead workstreams, and mentor engineers.
Requirements
- Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
- At least 7.5 years of overall experience is stated; the qualifications also specify at least four years of experience building, operating, and designing AI/ML infrastructure or related 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.
- 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, 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.
- Proven experience leading AI projects or engineering workstreams, evaluating AI technologies and architecture patterns, and managing multiple initiatives.
- Preferred qualifications include GCP certifications, regulated-industry experience, LLM infrastructure, vector databases, retrieval pipelines, accelerator scheduling, low-latency model serving, model optimization, enterprise architecture governance, FinOps, and vendor collaboration.
- Strong problem-solving, communication, collaboration, and stakeholder-alignment skills are required.
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.