Description

Key Responsibilities

• Develop and implement traditional AI/ML models to solve complex business and analytical problems.
• Train, evaluate, optimize, and retrain machine learning models using real-world datasets.
• Build scalable and production-ready machine learning solutions.
• Develop model pipelines and workflows for continuous training and improvement.
• Collaborate with data engineering, technology, and business teams to identify opportunities for AI/ML applications.
• Evaluate model performance and implement improvements to accuracy, scalability, and reliability.
• Apply appropriate statistical and machine learning techniques to support data-driven decision-making.
• Contribute to the design and implementation of modern AI/ML solutions, including Generative AI and Agentic AI where applicable.

Required Qualifications

• Strong professional experience as a Data Scientist / Machine Learning Engineer.
• Hands-on experience developing and deploying traditional AI/ML models.
• Strong understanding of machine learning algorithms, model training, evaluation, and optimization.
• Experience with model retraining and lifecycle management.
• Strong programming and data analysis skills, preferably with Python.
• Ability to work with large and complex datasets and translate business requirements into ML solutions.

Preferred Qualifications

• Experience with AWS SageMaker.
• Experience with Google Vertex AI.
• Background in Life Sciences, Healthcare, Clinical Data, or Radiology Imaging.
• Experience with Generative AI.
• Experience developing or working with Agentic AI solutions.
• Experience taking ML models from experimentation through production deployment

Key Skills

Education

Any Gradute