Lead DevOps Engineer

Responsibilities:

• Multi-cloud substrate: Production infrastructure across AWS, GCP, and Azure.
• Private interconnects (Direct Connect, Cloud Interconnect, ExpressRoute), transit/hub-spoke topologies, and cross-cloud latency managed as an explicit budget p95 per hop in tens of milliseconds, not "best effort. "
• Real-time media plane: Self-hosted LiveKit and SIP infrastructure at scale.
• Media servers are stateful; you'll design session-affine, event-driven autoscaling (KEDA-class) that survives traffic tripling within an hour.
• Model-serving infrastructure: GPU fleets (A100/H100/B200-class) for self-hosted ASR and open-weight LLMs inference optimisation, prefix caching, sticky-session routing, sub-500ms TTFT budgets alongside managed APIs (Vertex AI/Gemini, Bedrock, Azure).
• Vendor failover is your design, not your incident.
• Reliability and observability: OTel-native tracing (Grafana/Tempo stack), per-turn latency attribution across telephony/ASR/LLM/TTS, automated incident response and self-healing. You'll act as incident commander for infrastructure and write the runbooks you'd want at 3 a. m.
• Cost engineering: Cost-per-minute is an SLO here. We cut per-minute serving cost ~18x in six months through caching, rightsizing, autoscaling, and workload re-architecture; you'll own the next 10x.
• Security and compliance: Zero Trust across clouds: private endpoints/PrivateLink, IAM/RBAC, secrets management with rotation, WAF/DDoS protection.
• Operate controls for SOC 2 and ISO/IEC 27001 working knowledge of ISO/IEC 42001:2023 (AI management systems) hands-on preferred, rigorous theoretical grounding acceptable.
• You'll face bank and telecom auditors directly, including data-residency requirements.
• Technical leadership: Terraform-first IaC standards, production-readiness reviews, mentoring SREs.
• Lead means you raise the floor of the whole team.
• Problems on our plate right now.
• Scaling stateful, self-hosted media servers past current concurrency ceilings; HPA on CPU doesn't cut it.
• Migrating LLM inference from managed APIs to self-hosted open-weight models on GPUs without breaking TTFT budgets.
• ASR, LLM, and telephony living in different clouds: interconnect topology that keeps the packet path short and private.
• Multi-region DR that satisfies bank audits without doubling spend.
• If these read as interesting rather than terrifying, keep reading.

Requirements:

• 6+ years hands-on cloud infrastructure; 3+ years operating multiple clouds simultaneously in production; deep expertise in at least two of AWS/GCP/Azure.
• Real-time audio/video systems in production: WebRTC, SIP/PSTN, or streaming media; you've debugged jitter, not just read about it.
• Networking depth: VPC/VNet design, load balancing, DNS, NAT; private connectivity (Direct Connect / Cloud Interconnect / ExpressRoute, PrivateLink / Private Service Connect); transit gateways and cross-cloud mesh.
• Kubernetes at scale (EKS/GKE/AKS), Helm, and scaling stateful workloads; service mesh familiarity (Istio/Linkerd).
• Infrastructure as Code: Terraform (non-negotiable) across multi-account/multi-project estates; drift is a bug.
• AI/ML serving in production: GPU allocation and scheduling, inference servers or serverless GPU platforms (vLLM / Triton / Modal / Baseten-class), streaming protocols (WebRTC, WebSocket, gRPC).
• Production STT/TTS/LLM API operations: streaming integrations, quota management, multi-vendor failover (Deepgram / Google / Azure / Whisper-class ASR; ElevenLabs / Azure-class TTS).
• Security fundamentals: IAM/RBAC, secrets management (Vault or cloud-native), encryption and key rotation.
• CI/CD: GitHub Actions or GitLab CI with security scanning integrated into the pipeline.

Strong signal (nice-to-have):

• LiveKit, pipecat, Twilio, or comparable real-time platforms; SIP trunking and PSTN integration.
• KEDA or other event-driven autoscaling used in anger.
• MLOps: model versioning, canary and A/B rollout.
• FinOps discipline: reserved/spot strategy, unit-economics reporting.
• Certifications: AWS SA Professional, GCP Professional Cloud Architect, Azure Solutions.
• Architect Expert.
• ISO/IEC 42001:2023 exposure.

Good to Have:

• Single-cloud depth with documentation-level knowledge of the other two.
• Tool-checklist DevOps without production AI/ML serving scars.
• "Can learn quickly" as the primary qualification; this role needs day-one production credibility.
• Anyone who has never traced a packet across a cloud boundary.