Platform Architect

The Role

As a Cloud + DevOps Platform Architect, you will own the cloud infrastructure, deployment automation, CI/CD, observability, reliability, and security posture of the platform. The platform will be deployed across multiple clouds, including AWS, Azure, and GCP, and will integrate with modern data platforms such as Databricks and Snowflake.

This role requires a strong engineering mindset with hands-on ownership of infrastructure-as-code, Kubernetes, containerization, GitOps, enterprise packaging, and secure deployment patterns.

The role is not limited to managing internal infrastructure. The platform must be deployable into enterprise customer environments, including private VPCs, hybrid cloud, on-premise, restricted network, and air-gapped environments.

Key Responsibilities

• Design, build, and operate multi-cloud deployment architecture across AWS, Azure, and GCP.
• Build and maintain infrastructure-as-code using Terraform, OpenTofu, or equivalent IaC frameworks for repeatable multi-cloud deployments.
• Build CI/CD pipelines for application services, data pipelines, AI agents, model artifacts, infrastructure changes, and environment promotions.
• Implement GitOps-based deployment patterns using tools such as Argo CD, Flux, GitHub Actions, GitLab CI/CD, Azure DevOps, or equivalent platforms.
• Design and operate containerized workloads using Docker and Kubernetes, including EKS, AKS, GKE, and customer-managed Kubernetes clusters.
• Author and maintain production-grade Helm charts for platform deployment, including parameterized configurations, environment-specific overrides, upgrade paths, secrets injection, and rollback support.
• Support enterprise deployment models, including private VPC, hybrid cloud, on-premise, restricted-network, and air-gapped environments.
• Own observability, alerting, logging, tracing, health checks, SRE practices, incident response, and platform reliability metrics.
• Build deployment packaging with a product engineering mindset, including versioning, upgrade/rollback, dependency isolation, customer-facing configuration, and environment validation.
• Implement security controls covering Kubernetes RBAC, identity and access management, secrets management, network policies, encryption, image scanning, audit logging, and vulnerability management.
• Lead or support SOC 2 / ISO 27001 readiness, security control mapping, evidence collection, security questionnaires, and customer security reviews.
• Implement secrets management using tools such as HashiCorp Vault, Sealed Secrets, cloud-native secret managers, or equivalent technologies.
• Implement image scanning and supply chain security using tools such as Trivy, Snyk, Grype, Cosign, SBOM tooling, or equivalent platforms.
• Support MLOps and model versioning infrastructure, including model artifact management, model registry integration, DVC or equivalent data/model versioning, and AI workload deployment automation.
• Package Python-based AI agents into lightweight, secure, production-ready Docker images with proper dependency isolation and runtime controls.
• Support GPU-enabled workloads on Kubernetes, including NVIDIA device plugins, GPU scheduling, CUDA version compatibility, node pools, and containerized inference runtimes such as vLLM or Ollama, where required.
• Partner with engineering teams to enforce secure-by-default patterns across application, data, AI, and infrastructure layers.
• Optimize infrastructure for cost, reliability, performance, scalability, and enterprise deployment flexibility.

Must-Have Qualifications

• 10–12 years of experience in DevOps and SRE, Cloud Engineering, Platform Engineering, or Infrastructure Engineering with production responsibility.
• Deep hands-on experience with at least two of the following cloud platforms: AWS, Azure, GCP.
• Strong hands-on experience with Terraform, OpenTofu, or equivalent infrastructure-as-code frameworks.
• Strong experience with CI/CD pipelines, release automation, environment promotion, rollback strategies, and deployment governance.
• Hands-on experience with Docker, Kubernetes, Helm, and container orchestration platforms such as EKS, AKS, GKE, or enterprise Kubernetes distributions.
• Experience authoring or significantly customizing Helm charts, including values management, secrets handling, configuration templating, and upgrade lifecycle management.
• Strong observability experience using tools such as Prometheus, Grafana, ELK / OpenSearch, Loki, Jaeger, Datadog, New Relic, Splunk, or equivalent platforms.
• Working knowledge of SOC 2 controls, ISO 27001 controls, identity management, access control, secrets management, and customer security questionnaires.
• Experience implementing Kubernetes security controls, including RBAC, network policies, pod security standards, image scanning, secrets encryption, and least-privilege access.

Good to Have

• Certified Cloud Architect certification from AWS, Azure, or GCP.
• Security certifications such as CISSP, CCSP, CKS, CKAD, CKA, or equivalent.
• Experience with Databricks, Snowflake, vector databases, model registries, or AI platform infrastructure.
• Experience with MLflow, DVC, Kubeflow, KServe, Seldon, Ray, vLLM, Ollama, or similar AI/ML deployment technologies.

Why This Role Is Exciting

You will be one of the most senior technical voices in the founding team. You will be defining the platform architecture, engineering patterns, AI safety controls, and cloud design principles from the ground up.

This role gives you the opportunity to build the deployment backbone for a venture-backed Infosys platform from day one. You will shape how the platform is installed, secured, monitored, upgraded, and operated across cloud, hybrid, on-premise, and restricted enterprise environments.