Senior Multi-Cloud AI DevOps Engineer

5 - 6 years

6 - 11 Lacs

Posted:12 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

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

Required Qualifications

Experience

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)

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Skit.ai

Artificial Intelligence, Voice Technology

Milpitas

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