Key Responsibilities :

- 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