Machine Learning Engineer

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 Tensor Flow, 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, No
  
  SQL, 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.