Senior Multi-Cloud AI DevOps Engineer

About Us Skit.ai is the pioneer Conversational AI company transforming collections with omnichannel GenAI-powered assistants. Skit.ai's Collection Orchestration Platform, the world's first solution, streamlines collection conversations by syncing channels and accounts. Skit.ai's Large Collection Model (LCM), a collection LLM, powers the strategy engine to optimize interactions, enhance customer experiences, and boost bottom lines for enterprises. Skit.ai has received several awards and recognitions, including the BIG AI Excellence Award 2024, Stevie Gold Winner 2023 for Most Innovative Company by The International Business Awards, and Disruptive Technology of the Year 2022 by CCW. Skit.ai is headquartered in New York City, NY. Visit https://skit.ai/ Job Title : Senior Multi-Cloud AI DevOps Engineer Location : Bangalore (Full Time On Site) Experience : 6+ years Type : Full-time Key Responsibilities Multi-Cloud Infrastructure Architecture Design production-grade infrastructure across AWS, GCP, and Azure Architect private, low-latency interconnects between clouds AWS Direct Connect GCP Cloud Interconnect Azure ExpressRoute Dedicated cross-cloud networking solutions Deploy multi-region infrastructure for HA and DR Implement IaC (Terraform, Pulumi, CloudFormation) across all clouds AI/ML Services & API Integration Deploy and optimize Google Gemini APIs, Vertex AI APIs, Bedrock APIs Implement ASR/STT services Deepgram Google Cloud Speech-to-Text Azure Speech Services Whisper Baseten More Configure TTS services Google Cloud TTS Azure Speech ElevenLabs Implement model serving infrastructure for fine-tuned models Security & Network Engineering Design Zero Trust network architectures across multi-cloud Configure VPCs, VNets, security groups, NACLs, firewall rules Implement private endpoints and PrivateLink configurations Set up VPN tunnels, peering connections, transit gateways Implement secrets management, encryption, key rotation Maintain compliance: SOC 2, ISO 27001, ISO/IEC 42001, if not practical, theoretical understanding of AI regulated compliances like ISO/IEC 42001:2023, ISO/IEC 27001is must Compute & Container Orchestration Create and manage VMs, instance groups, auto-scaling Deploy Kubernetes clusters (EKS, GKE, AKS) Implement GPU compute infrastructure NVIDIA A100, H100 TPUs Optimize compute costs while meeting performance SLAs Performance & Reliability Design for sub-100ms latency in voice AI pipelines Implement monitoring and observability Datadog Grafana CloudWatch Cloud Monitoring Build automated incident response and self-healing infrastructure Conduct performance testing, load testing, capacity planning Experience Required Qualifications 6+ years hands-on cloud infrastructure experience 3+ years working across multiple cloud providers simultaneously Proven track record with production AI/ML workloads Deep expertise in at least 2 of: AWS, GCP, Azure Experience with real-time voice/audio systems Technical Skills — Must Have VM Management AWS EC2 GCP Compute Engine Azure VMs Creation, configuration, hardening, lifecycle management Advanced Networking VPCs, subnets, route tables, NAT gateways Load balancers (ALB, NLB, Cloud Load Balancing, Azure LB) DNS (Route 53, Cloud DNS, Azure DNS) Private Connectivity VPN tunnels Direct Connect / Cloud Interconnect / ExpressRoute PrivateLink / Private Service Connect Cross-Cloud Networking Transit gateways Hub-spoke architectures Multi-cloud mesh Container Orchestration Kubernetes (EKS, GKE, AKS) Docker, Helm Service mesh (Istio, Linkerd) Infrastructure as Code Terraform (required) CloudFormation Pulumi ARM templates Security IAM, RBAC Security groups, NACLs WAF, DDoS protection Secrets management (Vault, Secrets Manager) CI/CD GitHub Actions, GitLab CI Cloud Build, CodePipeline Security scanning integration AI/ML Infrastructure — Must Have Google Gemini APIs / Vertex AI or equivalent LLM platforms Production STT deployment (Deepgram, Google Speech, Azure Speech, Whisper) Production TTS deployment (Google TTS, Azure TTS, ElevenLabs) Model serving patterns, GPU allocation, inference optimization Real-time streaming protocols (WebRTC, WebSocket, gRPC) Nice to Have LiveKit, Twilio, or similar real-time communication platforms Telephony/VoIP background (SIP trunking, PSTN integration) MLOps: model versioning, A/B testing, canary deployments FinOps: cost optimization, reserved/spot instances Certifications AWS Solutions Architect Professional GCP Professional Cloud Architect Azure Solutions Architect Expert AI governance frameworks (ISO/IEC 42001:2023) What We're NOT Looking For Someone who can learn quickly — we need proven production experience Single-cloud Specialists Who Only Know Others From Documentation DevOps generalists without deep AI/ML infrastructure experience Candidates without hands-on cross-cloud connectivity experience