Teams at webAI

Designs and scales secure infrastructure for AI workloads across multi-cloud, hybrid, and edge environments. Builds Infrastructure as Code and GitOps workflows, operates Kubernetes clusters for AI/ML workloads, develops secure CI/CD pipelines, supports MLOps automation, and implements observability, access management, compliance, incident response, and disaster recovery. The role also includes infrastructure documentation and mentoring junior and mid-level engineers.

Serve as a technical advisor for strategic industrial accounts across manufacturing, energy, and supply chain sectors. Develop AI solution roadmaps, architect edge deployments, lead technical workshops and proofs of concept, assess feasibility and retrofit strategies, and coordinate solution architects, engineers, product teams, sales, and delivery partners. Translate executive business goals and operational requirements into scalable AI, automation, and infrastructure solutions while driving customer adoption and long-term value.

Design and implement secure, reliable synchronization and networking systems for distributed AI across edge devices, cloud environments, and air-gapped government systems. Architect frameworks using content-addressable storage, Merkle trees, and CRDTs; optimize distributed software under resource constraints; apply threat modeling and cryptography; collaborate on restricted-system deployments; and maintain testing, observability, and compliance readiness. The role requires 25–50% travel to offices, government field sites, and test locations.