DevOps Engineer - ELK Stack

Company Description

TrueFan uses proprietary AI technology to connect fans and celebrities and is now focused on revolutionising customer-business interactions with AI-powered personalized video solutions. Our platform enables brands to create unique, engaging video experiences that drive customer loyalty and deeper connections.

Job Description

Role : DevOps Engineer

Company Overview

We are a cutting-edge AI company focused on developing advanced lip-syncing technology using deep neural networks.

Our solutions enable seamless synchronisation of speech with facial movements in videos, creating hyper-realistic content for various industries such as entertainment, marketing, and more.

Key Responsibilities

Model Training/Deployment Pipelines and Monitoring :

• Design, implement, and maintain scalable and automated pipelines for deploying deep neural network models.
• Monitor and manage Production models, ensuring high availability, low latency, and smooth performance.
• Automate workflows for data preprocessing (face alignment, feature extraction, audio analysis), model retraining, and video generation.
• Implement Logging, Tracking, and Monitoring Systems to ensure data integrity and visibility into the model lifecycle.
  
  

Infrastructure Management

• Build and manage cloud-based infrastructure (AWS, GCP, or Azure) for efficient model training, deployment, and data storage.
• Collaborate with DevOps to manage containerization (Docker, Kubernetes) and ensure robust
• CI/CD pipelines using github and jenkins for model delivery.
• Monitor resource for GPU/ CPU-intensive tasks like video processing, model inference, and training using Prometheus , Grafana, alert manager, ELK stack.
  
  

Collaboration

• Work closely with ML engineers to integrate models into production pipelines.
• Provide tools and frameworks for rapid experimentation and model versioning.
  
  

Required Skills

• Basic Python
• Strong experience with cloud platforms (AWS, GCP, Azure) and cloud-based machine learning services.
• Expert knowledge of containerization technologies (Docker, Kubernetes) and infrastructure- as-code (Terraform, CloudFormation)
• Have understanding of Deployment of both synchronous and asynchronous API using Flask, Django, Celery, Redis, RabbitMQ , Kafka
• Deployed and Scaled AI/ML in Production.
• Familiarity with deep learning frameworks (TensorFlow, PyTorch).
• Familiarity with video processing tools like FFMPEG and Dlib for handling dynamic frame data.
• Basic understanding of ML models
  
  

Preferred Qualifications

• Experience in image and video-based deep learning tasks.
• Familiarity with media streaming and video processing pipelines for real-time generation.
• Experience with real-time inference and deploying models in latency-sensitive environments.
• Strong problem-solving skills with a focus on optimising machine learning model infrastructure for scalability and performance.
  
  

(ref:hirist.tech)