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