Agentic AI Infrastructure Lead
2 weeks ago
Your Role:
As the Agentic and Generative AI Infrastructure Lead, you will architect and manage the foundational infrastructure that powers enterprise-scale AI innovation. This role is central to enabling the secure, scalable, and cost-effective deployment of Agentic AI workloads across multi-tenant and multi-service environments. You will be responsible for orchestrating the technical backbone that supports AI agents as they interact with APIs, core data systems, and hybrid cloud environments - including legacy and third-party platforms.
With a strategic builders mindset, you will lead the design and implementation of infrastructure that supports the full AI lifecycle - from development and training to deployment and monitoring. Your work will ensure that AI systems are not only performant and reliable but also governed by robust access controls, cost containment strategies, and compliance safeguards. This role is pivotal in balancing innovation with operational discipline, ensuring that AI capabilities scale responsibly across the organization.
Your Profile (needed):
- Strong technical background with a degree in management or computer science, and proficiency in SQL and Python for data analytics.
- At least 2 years of experience building and managing enterprise-grade cloud infrastructure (AWS, Azure, GCP), with a focus on multi-tenant and multi-service architectures.
- Deep understanding of AI orchestration, including data flow management, computational resource optimization, and coordination of multiple AI models.
- Proven ability to implement robust security and access controls, including RBAC, network policies, and isolation techniques.
- Experience with cost containment strategies for cloud-based AI deployments, including awareness of API call fees and Retrieval-Augmented Generation (RAG) training costs.
- Foundational understanding of Agentic AI concepts and their application in enterprise environments.
Your Profile (nice to have):
- Background in technical service design.
- Experience with web application deployment and container orchestration for AI/ML workloads.
- Knowledge of deployment models (on-premise vs. public cloud) and their trade-offs.
- Familiarity with audit logging and cost allocation in multi-tenant environments.
- Awareness of the Agentic AI development lifecycle, including deployment automation and workflow optimization.
- Consulting experience or a client-facing background in automation or infrastructure roles
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