Our client is looking for machine learning engineers to develop and implement machine learning models and algorithms to drive actionable insights and solutions. You will collaborate closely with cross-functional teams to identify business needs, design experiments, and deploy scalable machine learning solutions. This role offers an exciting opportunity to work on diverse projects and make a significant impact in a dynamic and fast-paced environment.
- Research, design, and implement machine learning models and algorithms to address business challenges and opportunities.
- Collect, preprocess, and analyze data from various sources to extract meaningful insights and features.
- Develop and maintain scalable machine learning pipelines for training, evaluation, and deployment of models.
- Collaborate with software engineers to integrate machine learning solutions into production systems and applications.
- Evaluate and benchmark different machine learning techniques and frameworks to optimize performance and efficiency.
- Stay up-to-date with the latest advancements in machine learning research and technology to drive innovation and best practices within the team.
- Bachelor’s, Master’s, or Ph.D. degree in Computer Science, Engineering, Mathematics, Statistics, or related field.
- Proven experience in developing and deploying machine learning models in real-world applications.
- Proficiency in programming languages such as Python, R, or Java, as well as experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Strong understanding of machine learning algorithms, techniques, and best practices, including deep learning, supervised/unsupervised learning, and reinforcement learning.
- Experience with data preprocessing, feature engineering, and model evaluation/validation techniques.
- Excellent problem-solving skills and the ability to work independently and collaboratively in a team environment.
- Effective communication skills with the ability to translate complex technical concepts to non-technical stakeholders.
- Experience with distributed computing frameworks such as Apache Spark or Hadoop.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Knowledge of big data technologies and tools such as SQL, NoSQL, and data lakes.
- Experience with containerization and orchestration tools such as Docker and Kubernetes.
- Contributions to open-source projects or publications in machine learning conferences/journals.