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UptimeAI Inc.

DevOps Engineer

Posted Yesterday
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In-Office
Bangalore, Bengaluru Urban, Karnataka, IND
Mid level
In-Office
Bangalore, Bengaluru Urban, Karnataka, IND
Mid level
Designs and manages AWS and Azure cloud infrastructure for SaaS and customer deployments. Builds CI/CD pipelines, infrastructure-as-code, networking, monitoring, disaster recovery, security controls, and multi-tenant or single-tenant environments. Supports BYOC deployments, release management, cloud cost and performance optimization, and AI/ML workloads while collaborating with engineering in a fast-paced startup.
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DevOps Engineer

About UptimeAI:

UptimeAI provides AI reasoning agents that help industrial operations teams make goal-oriented, real-time decisions.

Industrial facilities are rich in data but constrained by the expertise required to maintain and interpret it. As experienced engineers retire and operational complexity grows, organizations struggle to scale the expert judgment needed to maintain reliability, safety, and performance.

UptimeAI addresses this challenge by embedding engineering reasoning directly into AI agents that continuously analyze operational data, diagnose issues, and guide teams toward the best actions. The result is faster decisions, reduced downtime, improved efficiency, and stronger operational performance.

 

Role: DevOps Engineer

Location: Bangalore, India (Hybrid)

Experience: 4+ years

Department: Engineering

Type: Full-time

About Us

We are a fast-growing, AI-first SaaS startup backed by top-tier investors and operating across India and the US. Our platform helps enterprises optimize critical business functions using cutting-edge AI and automation. As we scale, we’re looking for a hands-on DevOps Engineer who thrives in startup environments and can take ownership of cloud infrastructure, deployment, and CI/CD workflows.

Key Responsibilities

  • Design, implement, and manage cloud infrastructure across AWS and Azure for both internal platforms and customer-specific deployments

  • Configure and maintain VPCs, VPNs, and peering to enable secure, scalable, and isolated environments

  • Build and automate CI/CD pipelines for application and ML workloads

  • Manage multi-tenant vs single-tenant deployments based on customer requirements

  • Implement monitoring, alerting, logging, and disaster recovery strategies

  • Work closely with engineering to ensure seamless Dev→Prod flows and secure release management

  • Set up and manage infrastructure as code (e.g., Terraform, Pulumi, Bicep, CloudFormation)

  • Optimize costs, performance, and availability for both internal and customer-facing cloud workloads

  • Enforce security best practices, access control, and compliance across infrastructure

Requirements

  • 4+ years of experience as a DevOps/SRE/Cloud Engineer in high-growth SaaS or product startups

  • AWS Certified (at least Solutions Architect - Associate) and Azure Certified (e.g., AZ-104 or higher)

  • Strong experience with AWS and Azure networking, including: VPC, VPNs, Subnets, Route Tables, Security Groups, NAT Gateways

  • Site-to-site VPN setups for enterprise customers

  • Proven experience deploying applications to customer-controlled cloud environments (BYOC) and company-controlled SaaS environments

  • Expertise with tools like: CI/CD: GitHub Actions, GitLab CI, Azure Pipelines; IaC: Terraform, Bicep, or Pulumi; Containers: Docker, Kubernetes (EKS/AKS preferred)

  • Familiarity with Secrets Management, IAM, Role-based Access Control, and SSO/SAML integration

  • Strong scripting skills in Bash, Python, or PowerShell

  • Comfortable working in a fast-paced, ambiguous startup environment

Good to Have

  • Experience with AI/ML pipeline deployment or GPU workloads

  • Exposure to SOC2, ISO27001, or GDPR compliance in a cloud environment

  • Familiarity with tools like Prometheus, Grafana, Datadog, ELK, or Azure Monitor

Why Join Us

  • Be part of a foundational team shaping infrastructure strategy from the ground up

  • Solve real-world scaling and deployment problems for enterprise AI workloads

  • Competitive salary + equity

  • Fast-track learning with ownership, autonomy, and purpose

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