Machine Learning Engineer

Job Description: As a Machine Learning Engineer, you will be responsible for developing and implementing machine learning models and algorithms to solve complex problems and enhance our AI capabilities. You will collaborate with cross-functional teams to design, train, and deploy machine learning models that drive innovation and improve our products and services.

Key Responsibilities:
1. Develop and implement machine learning models and algorithms to solve business problems and improve AI capabilities.
2. Collaborate with data scientists and data engineers to collect, pre-process, and analyse large datasets to train and validate machine learning models.
3. Design and optimize machine learning pipelines for efficient data processing, feature engineering, model training, and evaluation.
4. Explore and experiment with various machine learning techniques, frameworks, and libraries to identify the most suitable approaches for different use cases.
5. Conduct research and stay up-to-date with the latest advancements in machine learning and AI technologies to propose innovative solutions.
6. Collaborate with cross-functional teams to integrate machine learning models into production systems and ensure scalability, reliability, and performance.
7. Monitor and evaluate the performance of deployed machine learning models, identify areas for improvement, and implement necessary enhancements.
8. Document and communicate technical concepts, methodologies, and results to both technical and non-technical stakeholders.
9. Stay informed about industry best practices, emerging trends, and ethical considerations related to machine learning and AI.

Qualifications:
1. Bachelor's or Master's degree with minimum 5 years of relevant experience. 
2. Banking is prefered, if not BFSI
3. Strong understanding of machine learning algorithms, statistical modelling, and data analysis techniques.
4. Proficiency in programming languages such as Python, R, or Java, and experience with machine learning libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
5. Solid knowledge of data pre-processing, feature engineering, and model evaluation techniques.
6. Experience with big data processing frameworks (e.g., Hadoop, Spark) and distributed computing environments.
7. Strong problem-solving skills and ability to work on complex projects with minimal supervision.
8. Excellent communication and collaboration skills to work effectively within a cross-functional team.
9. Experience with deploying machine learning models in production systems is a plus.
10. Knowledge of natural language processing (NLP), computer vision, or reinforcement learning is desirable.