Description :

About Us.

Skit.ai is the pioneer Conversational AI company transforming collections with omnichannel GenAI-powered assistants.

Skit.ais Collection Orchestration Platform, the worlds first solution, streamlines collection conversations by syncing channels and accounts.

Skit.ais 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.

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.

- 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.