24 days ago
Bengaluru, IndiaSenior
Responsibilities
- Own the end-to-end architecture and design of AWS compute infrastructure for large-scale AI/ML systems.
- Design and tune AWS GPU clusters, distributed training systems, container platforms, model-serving environments, networking, and high-throughput storage.
- Evaluate architecture alternatives and lead reviews addressing performance, security, compliance, reliability, cost, and operational complexity.
- Define infrastructure roadmaps, capacity plans, 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 performance and cost tracking.
- Integrate AI/ML systems into enterprise environments while maintaining interoperability, security, compliance, and regulatory alignment.
- Document architecture decisions, standards, runbooks, reusable infrastructure patterns, assumptions, and dependencies.
- Collaborate with clients, stakeholders, architects, and engineering teams to translate business requirements into architecture standards.
- Set technical direction, lead workstreams, review designs and code, and mentor engineers.
Requirements
- A bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field is required.
- At least 7.5 years of experience is required; 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 data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
- Strong understanding of AI/ML concepts and the infrastructure required to deploy, run, and optimize production AI workloads.
- Experience architecting GPU or accelerated compute, distributed training, model serving, high-throughput storage, container platforms, and secure cloud networking.
- Strong working knowledge of Terraform or CloudFormation, Docker, Kubernetes, InfraOps, MLOps, observability, and incident response practices.
- Experience optimizing AWS AI infrastructure for performance, power, cost, scalability, security, reliability, and compliance.
- Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable infrastructure patterns.
- Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
- Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
- AWS certifications and experience in regulated industries are preferred.
- Exposure to LLM infrastructure, vector databases, retrieval pipelines, GPU scheduling, high-performance storage, low-latency model serving, and model optimization techniques is preferred.
- Knowledge of enterprise architecture governance, FinOps, infrastructure partner or vendor collaboration, and production support operating models is preferred.
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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.