Role: Senior DevOps Engineer
Function: DevOps / Platform Engineering / SRE
Location: Bangalore
Type: Full-time
Industry: Consumer AI
About Company
The company is building the AI layer for Bharat at India-scale. It is backed by partnerships with global tech leaders including Meta and Google.
The mission is to make AI useful, reliable, and safe for everyday Indian consumers. The platform is engineered from day one for 100M+ users and 1B-ready constraints on latency, cost, reliability, and safety.
The team operates like a startup inside a high-scale ecosystem — small pods, high autonomy, and fast iteration.
Position Overview
This role owns the infrastructure backbone that every engineer, model, and product at the company depends on — from multi-region GCP infrastructure and multi-cluster Kubernetes environments to GPU compute for ML workloads and the CI/CD pipelines that ship at high velocity. You will build 0-to-1 platform infrastructure designed from day one for extreme scale, low latency, and high reliability, while supporting the specialised compute demands of real-time AI inference and training pipelines serving hundreds of millions of Indian users.
Role & Responsibilities
- Design, provision, and operate production-grade multi-region GCP infrastructure — owning the full stack from VPCs, IAM, and networking to compute, storage, and cloud security posture management
- Architect and operate multi-cluster GKE environments: cluster lifecycle management, autoscaling (HPA, VPA, KEDA, Cluster Autoscaler), workload scheduling, network policies, and upgrade strategies
- Build and maintain high-velocity CI/CD pipelines with blue-green, canary, and rolling deployment strategies; own GitOps workflows via ArgoCD or Flux for declarative, auditable Kubernetes delivery
- Provision, schedule, and optimise GPU node pools for ML training, fine-tuning, and real-time inference — including MIG/time-sharing configurations, spot/preemptible handling with checkpointing, and cost-aware autoscaling
- Build and own the full observability stack — metrics (Prometheus, Cloud Monitoring), structured logs, distributed traces (OpenTelemetry), and SLI/SLO dashboards — and lead end-to-end incident response including PIR documentation and preventive automation
- Implement and maintain full IaC coverage using Terraform at module/workspace scale — including state backend management, drift detection, and automated plan/apply pipelines
- Own cloud cost governance: resource tagging, utilisation monitoring, rightsizing, commitment-based discount strategies, and automated waste detection across GPU and general compute
Must Have Criteria
- 5–8 years in DevOps, SRE, or platform engineering with hands-on ownership of production systems at scale — not tooling maintenance or pipeline-only roles
- Deep GCP expertise (mandatory): GKE, Cloud Run, GCS, VPC, IAM, Secret Manager, Cloud Monitoring, Pub/Sub, and Artifact Registry in production environments
- Production-grade Kubernetes: multi-cluster lifecycle management, autoscaling architectures (HPA/VPA/KEDA/Cluster Autoscaler), RBAC, network policies, and workload isolation
- Terraform ownership at module/workspace scale: state backend management, drift detection, and automated apply pipelines in a multi-engineer team environment
- CI/CD pipeline design and ownership for multi-service, high-velocity environments including GitOps delivery with ArgoCD or Flux
- Hands-on GPU compute provisioning for ML workloads: NVIDIA node pools, device plugin configuration, MIG or time-sharing strategies, spot/preemptible handling, and cost-aware autoscaling
- Observability engineering from scratch: Prometheus, Grafana, structured logging, distributed tracing (OpenTelemetry), alerting policy design, and production incident response with RCA documentation and follow-through
- Working proficiency in at least one scripting/programming language — Python (preferred), Node.js, or Java — for automation, tooling, and infrastructure utilities; this is not primarily a coding role but technical depth matters
Nice to Have
- AI serving infrastructure experience: Triton Inference Server, vLLM, or Ray Serve — including model versioning and A/B traffic routing
- Service mesh implementation: Istio or Cilium for mTLS, traffic management, and zero-trust networking in production
- FinOps tooling: Kubecost, Infracost, or equivalent for granular GPU and cloud cost attribution and rightsizing
- Container security: image scanning (Trivy/Snyk), admission controllers (OPA Gatekeeper/Kyverno), and runtime security (Falco)
- Chaos engineering facilitation: Litmus, Chaos Monkey, or structured game-day exercises for platform resilience validation
What We Offer
- Foundational infrastructure ownership at India-scale — systems built for 100M+ users from day one, with a clear path to 1B-ready architecture
- Direct exposure to cutting-edge AI compute infrastructure: GPU clusters, real-time inference serving, and multimodal ML pipelines
- Startup-pace execution within the company's high-scale ecosystem — small pod, high autonomy, fast iteration, and outsized impact on a category-defining consumer AI product
- Collaboration with ML engineers, platform architects, and product leadership on systems that matter to hundreds of millions of Indian users
