- Model Development & Implementation : Design, develop, train, and evaluate various machine learning models (e.g., supervised, unsupervised, deep learning) to solve specific business problems.

- Data Pipeline & Feature Engineering : Collaborate with data engineers to build robust data pipelines, perform extensive feature engineering, and ensure data quality and availability for model training.

- Model Deployment & MLOps : Implement and manage the deployment of ML models into production environments, leveraging MLOps best practices for versioning, monitoring, and retraining.

- Performance Optimization : Optimize the performance, scalability, and efficiency of ML models and their serving infrastructure.

- Research & Innovation : Stay up-to-date with the latest advancements in machine learning research and apply relevant techniques to improve existing models and explore new opportunities.

- Experimentation & A/B Testing : Design and execute A/B tests or other experimentation frameworks to validate model performance and impact in real-world scenarios.

- Code Quality & Best Practices : Write clean, well-documented, and testable code for ML pipelines, models, and services. Participate in and conduct code reviews.

- Collaboration : Work closely with data scientists, software engineers, product managers, and other stakeholders to understand business requirements, define problems, and integrate ML solutions effectively.

Required Skills & Qualifications :

- 4+ years of professional experience as a Machine Learning Engineer or in a similar role.

- Strong proficiency in Python and its ML ecosystem (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

- Solid understanding of core machine learning algorithms and concepts (e.g., regression, classification, clustering, ensemble methods, neural networks).

- Experience with data preprocessing, feature engineering, and model evaluation techniques.

- Hands-on experience with MLOps principles and tools for model deployment, monitoring, and lifecycle management.

- Proficiency with version control systems, particularly Git.

- Experience with cloud platforms (e.g., AWS, Azure, GCP) and their ML services is highly desirable.

- Familiarity with containerization technologies (e.g., Docker, Kubernetes) is a plus.

- Strong problem-solving, analytical, and statistical skills.

- Excellent communication (verbal and written) and collaboration abilities.

- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative field