DevOps Engineer

Auric AI Labs; Bengaluru; On-site; INR 900,000 - 1,200,000; Full time

Job summary

Auric AI Labs in Bengaluru seeks a DevOps Engineer to co-manage our infrastructure across on‑prem, cloud, and network security, improving observability, automated deployments, and robust CI/CD practices. You'll implement infrastructure-as-code, design CI/CD pipelines, containerize ML workflows, and support ML lifecycle tooling with a strong emphasis on security, reliability, and ownership.

You'll work with the engineering team to mature the platform, monitor systems, and reduce manual toil

Qualifications

• 1+ years of hands-on DevOps, SRE, or infra roles.
• Networking and security know-how: firewalls, VPNs, DNS, TLS, hardening.
• Experience with Linux server provisioning, users, services, troubleshooting.
• Familiarity with at least one major cloud provider (AWS/GCP/Azure).
• Experience with containers (Docker) and scripting (Bash/Python).
• Exposure to CI/CD concepts and tooling (GitHub Actions, GitLab CI, Jenkins).
• Interest in MLOps and willingness to learn ML lifecycle tools.

Responsibilities

• Co-manage server infrastructure provisioning, hardening, backups and access control.
• Support firewall and network security operations, VPN access, segmentation and anomaly monitoring.
• Administer cloud resources, IAM, cost monitoring and security configuration.
• Design and implement CI/CD pipelines for automated testing and deployment.
• Introduce infrastructure-as-code (Terraform/Ansible) for reproducible environments.
• Establish observability with metrics, logging, alerting, and dashboards.
• Support ML workflows with pipeline automation, experiment tracking, and model deployment tooling.

Skills

Cloud (AWS/GCP/Azure)

Linux administration

Networking & security

CI/CD

Terraform/Ansible

Docker

Python/Bash scripting

MLOps interest

Tools

Kubernetes

Prometheus/Grafana

ELK

GitHub Actions

GitLab CI

Job description

We're looking for a DevOps Engineer to co-manage and strengthen our infrastructure, spanning on-premises servers, network security, and our cloud environment. Working alongside the engineering team, you'll help mature our platform through improved observability, automated deployments, and robust CI/CD practices.

The core focus of this role is DevOps fundamentals, including infrastructure, networking, and automation, with a growing footprint in MLOps as our machine learning workflows move toward production-grade tooling.

Responsibilities

Infrastructure & Security:

• Co-manage server infrastructure: provisioning, hardening, patching, backups, and access management
• Support firewall and network security operations: rule management, VPN access, segmentation, and anomaly monitoring
• Administer cloud resources: services, IAM, cost monitoring, and security configuration

Automation & Tooling:

• Design and implement CI/CD pipelines for automated testing and deployment
• Introduce infrastructure-as-code to make environments reproducible and well-documented (Terraform, Ansible, or similar)
• Establish observability across servers, network, and applications: metrics, logging, alerting, and dashboards
• Reduce manual operational work through automation

MLOps:

• Support ML workflows with pipeline automation, experiment tracking, and model deployment tooling
• Containerize and serve models, with monitoring for model and data health
• Contribute to establishing reproducible, versioned ML practices

Requirements

• 1+ years of hands-on experience in DevOps, systems administration, SRE, or infrastructure-focused roles
• Working knowledge of networking and network security: firewalls, VPNs, DNS, TLS, ports/protocols, and hardening practices
• Experience administering Linux servers (provisioning, users and permissions, services, troubleshooting)
• Familiarity with at least one major cloud provider (AWS, GCP, or Azure)
• Experience with containers (Docker) and scripting (Bash and/or Python)
• Exposure to CI/CD concepts and tooling (GitHub Actions, GitLab CI, Jenkins, etc.)
• Interest in MLOps and willingness to learn the ML lifecycle: training pipelines, model deployment, and monitoring
• Strong ownership mindset and clear communication around security and reliability trade-offs

Nice to Have

• Experience managing on-premises infrastructure (physical servers, local networking, hypervisors)
• Hands-on exposure to MLOps tooling (MLflow, Kubeflow, Airflow, model serving frameworks)
• Infrastructure-as-code experience (Terraform, Ansible, Pulumi)
• Kubernetes or other container orchestration experience
• Monitoring and observability stack experience (Prometheus, Grafana, Loki, ELK)
• GPU workload or ML infrastructure exposure
• Relevant certifications (cloud provider associate-level, networking, or security)