DevOps Engineer with ML Ops

3 weeks ago

Delhi, India TrueFan Full time
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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