Machine Learning Engineer (Mid & Senior)
Position: Machine Learning Engineer
Key Responsibilities
Model Development & Deployment:
Design, build, and optimize end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, and deployment.
Implement best practices for model versioning, testing, and continuous integration/continuous deployment (CI/CD) in production environments.
Data Analysis & Feature Engineering:
Work with large datasets to extract, clean, and prepare data for modeling. Develop innovative algorithms and robust statistical models to solve complex business challenges.
Collaboration & Communication:
Collaborate with cross-functional teams (data science, software engineering, product management) to integrate machine learning solutions into core products.
Present findings and model insights to technical and non-technical stakeholders.
Performance Monitoring & Optimization:
Monitor and evaluate model performance post-deployment; identify, troubleshoot, and resolve production issues.
Stay current with emerging trends and technologies in machine learning and propose enhancements to current systems.
Required Qualifications
Bachelors or Masters degree in Computer Science, Data Science, Electrical Engineering, Mathematics, or a related field.
Experience in machine learning engineering or a similar role.
Proficiency in Python and experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Solid understanding of statistical methods, data structures, and algorithm design. Experience with data processing tools and frameworks (e.g., Pandas, NumPy) and familiarity with SQL.
Practical experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker, Kubernetes) is a plus.
Managing big data (5+ years) is a plus.
Experience in banking, iGaming, or fintech is a plus.
Preferred Qualifications
Experience in MLOps, including model monitoring and automated deployment. Familiarity with deep learning, natural language processing, or computer vision applications. Proven track record of building and deploying scalable machine learning solutions in a production environment.
Experience with recommendation systems is a plus.
Experience with predicting the life cycle of customers is a plus.
Excellent teamwork skills and a passion for continuous learning and innovation.
Apply Now
Key Responsibilities
Model Development & Deployment:
Design, build, and optimize end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, and deployment.
Implement best practices for model versioning, testing, and continuous integration/continuous deployment (CI/CD) in production environments.
Data Analysis & Feature Engineering:
Work with large datasets to extract, clean, and prepare data for modeling. Develop innovative algorithms and robust statistical models to solve complex business challenges.
Collaboration & Communication:
Collaborate with cross-functional teams (data science, software engineering, product management) to integrate machine learning solutions into core products.
Present findings and model insights to technical and non-technical stakeholders.
Performance Monitoring & Optimization:
Monitor and evaluate model performance post-deployment; identify, troubleshoot, and resolve production issues.
Stay current with emerging trends and technologies in machine learning and propose enhancements to current systems.
Required Qualifications
Bachelors or Masters degree in Computer Science, Data Science, Electrical Engineering, Mathematics, or a related field.
Experience in machine learning engineering or a similar role.
Proficiency in Python and experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Solid understanding of statistical methods, data structures, and algorithm design. Experience with data processing tools and frameworks (e.g., Pandas, NumPy) and familiarity with SQL.
Practical experience with cloud platforms (AWS, Google Cloud Platform, or Azure) and containerization (Docker, Kubernetes) is a plus.
Managing big data (5+ years) is a plus.
Experience in banking, iGaming, or fintech is a plus.
Preferred Qualifications
Experience in MLOps, including model monitoring and automated deployment. Familiarity with deep learning, natural language processing, or computer vision applications. Proven track record of building and deploying scalable machine learning solutions in a production environment.
Experience with recommendation systems is a plus.
Experience with predicting the life cycle of customers is a plus.
Excellent teamwork skills and a passion for continuous learning and innovation.
Apply Now