DevOps Engineer - Agentic AI Platform

Advisor360° is a leading SaaS platform in the fintech space, transforming how wealth management firms operate. The DevOps Engineer role focuses on provisioning and operating AI infrastructure while integrating AI into DevOps processes to enhance operational efficiency.

Responsibilities:

• Provision and operate AI infrastructure: the Kubernetes, identity, secrets, and gateway layers that AI and agentic services depend on—built so teams can ship LLM-powered features safely
• Apply AI to DevOps itself: build and operate agent-assisted automation that reduces toil—triage, PR review, runbook generation, log and incident analysis. We already run AI in our delivery pipeline and want a teammate who'll take it further
• Cluster operations on AKS: node pool sizing, autoscaling policies, namespace isolation, and day-two operational hygiene across environments
• GitOps delivery with ArgoCD: app-of-apps structure, environment promotion, rollback strategy, and the guardrails that keep one team's bad deploy from cascading
• Deployment strategies: rolling, blue-green, and canary patterns for agentic services where a bad rollout has downstream effects on active workflows
• Platform reliability: SLIs, SLOs, alerting, and runbooks for the infra layer—so when something breaks at 2am, there's a playbook to follow (and you help write it)
• Cost and capacity management: AI workloads have spiky, non-linear cost profiles. You'll instrument and enforce budgets, quotas, and rightsizing across the cluster

Requirements:

• 3+ years operating Kubernetes in production
• Hands-on GitOps with ArgoCD: multi-environment setups, sync waves, health checks, and rollback under pressure
• Azure fluency: AKS, ACR, Azure Monitor, Key Vault, and managed/workload identity
• Infrastructure-as-code as a default: Terraform for everything—no console cowboys
• Scripting in Python, Go, or Bash for automation and tooling—maintained code, not one-offs
• Solid incident-response instincts; you've been on-call, written postmortems, and fixed the underlying conditions rather than just the symptom
• A real foothold in AI for infrastructure—either you've applied AI/LLMs to operations work (automation, triage, code or PR review, log analysis), or you've provisioned and operated infrastructure for AI workloads. You don't need an ML background; you need to be the DevOps engineer who's already reaching for AI and wants to go deeper
• • AI gateway / proxy patterns for AI workloads—centralized provider-key management, rate limiting, quotas, cost attribution, and failover in front of LLM providers
• • Agentic AI frameworks (LangGraph, AutoGen, or similar) and the infrastructure patterns they require
• • LLM inference / serving infrastructure (vLLM, TGI, Triton, or managed equivalents) and GPU capacity management
• • Policy-as-code with OPA/Gatekeeper for cluster governance
• • OpenTelemetry and distributed tracing across non-trivial services
• • Service mesh (Istio or Linkerd) for service-to-service auth and traffic management
• • Multi-tenant platform expertise