Senior DevOps Engineer

Omnidya IndiaPune, IN

4 hours ago

Job description

Job Title : DevOps Engineer

Location : Ahmedabad

Department : Engineering & Infrastructure

Reports To : CTO

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About Omnidya Tech LLP,

Hello

Omnidya is building India’s first advanced AI-powered dashcam ecosystem for fleet management, safety analytics, and smart transportation. Our platform fuses edge AI processing (ADAS, DMS, ANPR, telematics) with secure cloud connectivity (AWS IoT, S3, MQTT, and real-time streaming).

We are seeking a DevOps Engineer to scale our infrastructure, automate build and deployment pipelines, and manage GPU-based AI compute clusters both on-premise and in the cloud.

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Role Overview

As a DevOps Engineer, you will play a crucial role in automating deployments, managing distributed edge-cloud systems, and maintaining our GPU training and inference environments. You’ll work closely with the AI, firmware, and backend teams to ensure smooth CI / CD workflows, optimal GPU utilization, and high system reliability.

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Key Responsibilities

🧩 CI / CD & Automation

Design, build, and maintain CI / CD pipelines using GitLab CI, Jenkins, or GitHub Actions for backend, AI, and firmware builds.
Automate testing and deployment for Yocto-based embedded systems
Create Docker containers and deployment scripts for AI inference and cloud microservices.

☁️ Cloud & Infrastructure Management

Manage and scale AWS infrastructure (IoT Core, EC2, ECR, CloudWatch, Lambda, Route 53).

Set up and maintain Terraform or CloudFormation for Infrastructure as Code (IaC).

Implement robust monitoring, alerting, and log aggregation using Prometheus, Grafana, ELK, or CloudWatch.

⚙️ GPU Rack & Compute Cluster Management

Manage on-premise GPU servers / AI training racks (Ubuntu-based, multi-GPU systems).

Configure, optimize, and monitor GPU utilization for PyTorch / TensorFlow workloads.

Handle CUDA driver updates, containerized training environments, and model deployment pipelines.

Automate job scheduling using Slurm, Docker Swarm, or Kubernetes for GPU workloads.

Monitor performance metrics (GPU load, memory, thermals, power usage) to ensure stable training and inference operations.

📡 Device Integration & Fleet Management

Streamline OTA (Over-The-Air) update pipelines for connected edge devices.

Manage provisioning, authentication, and status monitoring of thousands of IoT devices.

Ensure robust MQTT, REST API, and video data sync between dashcams and the cloud.

🔒 Security & Compliance

Implement AWS IAM policies, TLS / SSL certificates, and secure OTA mechanisms.

Collaborate on device and cloud-level security hardening for regulatory compliance (BIS, ICAT).

📘 Documentation & Collaboration

Document automation flows, deployment topologies, and infrastructure standards.

Collaborate with AI, embedded, and backend teams to align deployment processes across systems.

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Required Skills & Experience

🎓 Experience

3–7 years of experience in DevOps, Cloud Infrastructure, or Site Reliability Engineering.

🛠️ Technical Skills

Linux system administration (Ubuntu, Yocto, Debian)

Containerization : Docker, Podman, Kubernetes (preferably K3s / MicroK8s)

CI / CD Tools : GitLab CI, Jenkins, GitHub Actions

Cloud Platforms : AWS (EC2, IoT Core, S3, Lambda, CloudWatch)

IaC : Terraform, CloudFormation

Monitoring : Prometheus, Grafana, ELK Stack

Networking : VPN, DNS, load balancing, NAT, SSL certificates

GPU Systems :

o Hands-on with NVIDIA GPU drivers, CUDA, cuDNN, TensorRT

o Experience with GPU workload management, thermal / power profiling, and optimization

o Familiarity with multi-GPU training, inference scaling, and model deployment

💡 Bonus Skills

Experience with embedded Linux (Yocto, NXP)

Understanding of RTMP / FLV streaming pipelines or GStreamer

Familiarity with Python microservices (FastAPI / Flask)

Knowledge of AI / ML model lifecycle management (training → quantization → edge inference)

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Soft Skills

Strong analytical and problem-solving mindset.

Excellent communication and cross-functional collaboration.

Passion for automation, reliability, and scalability.

Ability to work independently in a fast-paced startup environment.

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What We Offer

Competitive salary and performance-based bonuses.

Opportunity to work on cutting-edge edge-AI + GPU infrastructure projects.

Exposure to AWS, IoT, AI training clusters, and fleet-scale deployment systems.

Hybrid work setup and rapid growth opportunities in a high-impact product team.

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Senior Engineer • Pune, IN

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