AI/ML Engineer

Responsibilities:
Develop and maintain scalable, Django-based backend systems to support AI/ML workloads.
Implement and optimize Celery task management to handle high-volume user tasks (ranging from seconds to hours).
Deploy and manage Dockerized backend services in cloud environments.
Monitor and resolve performance bottlenecks in task queues, ensuring reliability and scalability.
Integrate training pipelines and model inference workflows seamlessly into the backend.
Contribute to API design for managing tasks, training jobs, and image generation.
 
Requirements:
3+ years of experience with Python, Django, and Celery.
Strong expertise in task queues (Redis or RabbitMQ) and distributed task handling.
Hands-on experience with Docker for containerization and deployment.
Proven ability to scale task systems dynamically to meet user demands.
Solid understanding of RESTful API development and performance optimization.
 
Nice to Have:
Familiarity with GPU cloud platforms (e.g., Vast.ai, RunPod, AWS EC2 GPU instances).
Experience working with AI/ML frameworks like PyTorch, TensorFlow, or Hugging Face Transformers.
Knowledge of CI/CD pipelines and orchestration tools like Kubernetes.
Understanding of image generation models (e.g., Stable Diffusion, LoRA fine-tuning).