DevOps Engineer - Agentic AI Platform

About the role

This role is hybrid, requiring three days per week onsite in our Needham, MA headquarters.

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
• 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

Qualifications

Bonus Points & Where You'll Grow

• 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

Skills

DevOps Engineer

Benefits

Not specified

Pay

$160,000–$175,000 + bonus & equity

Schedule

Hybrid