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Maven AGI, founded in July 2023 by executives from HubSpot, Google, and Stripe, is building an enterprise AI platform for autonomous customer support at scale. The company brings together talent from Google, Meta, Amazon, Microsoft, and Stripe, with advisors from OpenAI and other leading tech companies.
You will lead and evolve the infrastructure powering Maven AGI's AI platform as a DevOps Manager. This is a technical leadership role combining people management, operational ownership, and strong infrastructure judgment.
Leadership responsibilities include managing and developing a team of DevOps and infrastructure engineers, establishing clear expectations and accountability, partnering with technical leads on strategy alignment, hiring and onboarding as the team grows, and owning performance management and career development. You will balance reliability, security, customer commitments, and long-term platform investments while communicating infrastructure risks and progress to both technical and non-technical stakeholders.
Technically, you will guide the design and operation of cloud and on-premises infrastructure across Azure, AWS, and customer-managed environments. You'll oversee infrastructure-as-code practices using Pulumi, Bicep, or Terraform; own production Kubernetes environments including deployments, scaling, monitoring, and incident response; and drive CI/CD pipeline development for large-scale monorepos. You'll establish observability practices across metrics, logs, traces, and alerting, advance reliability practices including SLOs and disaster recovery, and support enterprise AI deployments with GPU infrastructure and model-serving workloads.
Required: 7+ years DevOps/SRE/Infrastructure experience, 3+ years managing teams, deep Kubernetes production expertise (AKS, EKS, GKE), strong infrastructure-as-code skills, CI/CD systems experience, proficiency in Python, Go, TypeScript, or Bash, solid understanding of IaaS providers and networking, monitoring/observability stack experience, multi-cloud or hybrid deployment experience, and strong communication skills. Nice-to-have skills include GPU infrastructure experience, ML/LLM serving workloads, Temporal workflow orchestration, and security/compliance background.
