Skit.ai·SaaS·5 months ago
5 months ago
AI Ops Engineer
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About this role
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: AI Ops 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
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)
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
SaaS
Company
Skit.aiSaaS
Bengaluru, India
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