MLOPS

Role:

• Develop and maintain scalable and efficient machine learning operations (MLOps) pipelines for deploying, monitoring, and managing deep learning models.
• Monitor model performance and implement strategies for continuous improvement.
• Design, build, and maintain data pipelines for processing, and storing datasets.
• Automate deployment workflows of deep learning models using cloud platforms (AWS, Azure, GCP) or on-premise solutions.
• Optimize machine learning solutions for latency, throughput, and cost.
• Collaborate with deep learning engineers to integrate deep learning models into production environments.

Required Skills and Qualifications:

• Master's (top 5%) or PhD in AI/ML, Computer Engineering, or Computer Science.
• Proficiency in Python
• Strong software engineering skills and ability to design modular and maintainable software solutions.
• Experience with deep learning frameworks such as PyTorch and TensorFlow.
• Knowledge of MLOps tools and related platforms (e.g., Neptune AI, MLflow).
• Understanding of machine learning lifecycle management, including model versioning, deployment, and performance monitoring.
• Ability to collaborate effectively with cross-functional teams.

Preferred Skills:

• Experience with C++ programming.
• Familiarity with deep learning inference libraries like ONNX or TensorRT.
• Experience in developing scalable HTTP services with tools like FastAPI or Flask.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
• Experience deploying AI solutions on edge devices.
• Knowledge of deep learning for computer vision and NLP tasks.
• Previous work experience in a related role.

We Offer:

• Full-time permanent contract.
• Competitive compensation and opportunities for career advancement.
• Collaborative and stimulating work environment.
• Training and mentorship opportunities, with access to cutting-edge research and technology.