• Model Development: Design, implement, and train machine learning models using state-of-the-art algorithms and frameworks including TensorFlow, PyTorch, scikit-learn
• Data Preparation: Process, clean, and transform large datasets for training and evaluation of ML models.
• Feature Engineering: Identify and engineer relevant features to optimize model performance and accuracy.
• Algorithm Optimization: Research and implement advanced algorithms to address specific use cases, including classification, regression, clustering, and anomaly detection.
• Integration: Collaborate with software developers to integrate ML models into production systems and ensure seamless operation.
• Performance Evaluation: Evaluate model performance using appropriate metrics and continuously optimize for accuracy, efficiency, and scalability.
• MLOps: Assist in setting up and managing CI/CD pipelines for model deployment and monitoring in production environments.
• Research and Development: Stay updated with the latest advancements in Gen AI AI/ML technologies and propose innovative solutions.
• Collaboration: Work closely with data engineers, product teams, and stakeholders to understand requirements and deliver tailored ML solutions.

Requirements Educational Background:

• Bachelor in Engineering in Computer Science, Data Science, Artificial Intelligence, or a related field.

Experience

• 3 to 6 years of hands-on experience in developing and deploying machine learning models.

Technical Skills

• Strong proficiency in Python and ML libraries/frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
• Experience with data manipulation tools like Pandas, NumPy, and visualization libraries such as Matplotlib or Seaborn.
• Familiarity with big data frameworks (Hadoop, Spark) is a plus.
• Knowledge of SQL/NoSQL databases and data pipeline tools (e.g., Apache Airflow).
• Experience with cloud platforms (AWS, Azure, Google Cloud) and their Gen AI AI/ML services.
• Strong understanding of supervised and unsupervised learning, deep learning, and reinforcement learning.
• Exposure to MLOps practices and model deployment pipelines.

Soft Skills

• Strong problem-solving and analytical skills.
• Effective communication and teamwork abilities.
• Ability to work in a fast-paced, collaborative environment.

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