Description

You will bridge the gap between data science prototyping and production-grade AI deployment.

Responsibilities

• Move AI applications from the prototyping stage into full production environments.
• Deploy and scale AI applications developed by Data Scientists to new business units.
• Automate the monitoring of applications and models.
• Extend global AI and GenAI foundations, including Python libraries, CI/CD pipelines, and reference architectures.
• Assist Data Scientists in designing AI solutions and selecting appropriate technologies.

Required Skills

• 7-9 years of professional experience.
• Strong proficiency in Python and ML model optimization.
• Hands-on experience with Cloud AI Services, specifically Azure ML.
• Cloud environment experience across Azure, AWS, or GCP.
• Experience with application industrialization using Docker and Kubernetes.
• Proficiency in DevOps practices, including Git and CI/CD pipelines.
• Expertise in MLOps, including model re-training, labeling, and monitoring.
• Knowledge of Generative AI architectures and solutions.
• Fluent English communication skills.

Preferred Skills

• MS degree in Data Science.

Key Skills

Education

Any Graduate