AI Infrastructure Specialist
2 weeks ago
We are seeking an experienced Cloud-Based MLOps Engineer to join our team at TrueFan. In this role, you will be responsible for designing and implementing scalable and automated pipelines for deploying deep neural network models.
The ideal candidate will have strong experience with cloud platforms (AWS, GCP, Azure) and cloud-based machine learning services, as well as expert knowledge of containerization technologies (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation).
You will also have a basic understanding of Python programming and experience with deployment of both synchronous and asynchronous API using Flask, Django, Celery, Redis, RabbitMQ , Kafka .
A strong problem-solving skills with a focus on optimising machine learning model infrastructure for scalability and performance is required.
Estimated Salary: $150,000 - $200,000 per year
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
- 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
- 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
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