DevOps Engineer

21 hours ago

Mangalore, India TrueFan Full time

Company Description

TrueFan uses proprietary AI technology to connect fans and celebrities and is now focused on revolutionizing 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: 


DevOps with MLOps 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.


  • Position: MLOps Engineer
  • We are looking for a talented and motivated MLOps Engineer to join our team. The ideal candidate will play a crucial role in managing and scaling our machine learning models and infrastructure, enabling seamless deployment and automation of our lip-sync video generation systems.


  • 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.

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