TrueFan - DevOps Engineer - Cloud Infrastructure

1 month ago


GurgaonGurugram, IN TrueFan Full time

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 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 :


- Work closely with ML engineers to integrate models into production pipelines.


- Provide tools and frameworks for rapid experimentation and model 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)
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