Machine Learning Engineer

What you will do

• Work closely with data scientists, software engineers, and business stakeholders to understand company goals and objectives. Translate that understanding into machine learning solutions that address business challenges and opportunities.
• Design, develop, and implement machine learning models and algorithms to solve complex problems across various domains, including but not limited to recommendation systems, natural language processing, and predictive analytics.
• Deploy machine learning models into production environments, collaborating with software engineering teams to ensure scalability, reliability, and maintainability.
• Monitor model performance and behavior in production, proactively identifying and addressing issues to maintain optimal performance and accuracy.
• Stay current with advances in machine learning research and technologies, exploring new approaches and methodologies to enhance model capabilities and effectiveness.
• Document methodologies, processes, and findings, sharing insights and best practices with the broader team to foster knowledge sharing and collaboration.

What skills and experience you bring

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; Master's or Ph.D. degree preferred.
• Minimum of 3 years of experience in machine learning engineering or related roles, with a strong track record of developing and deploying machine learning models in production environments.
• Knowledge developing and debugging in Python, GoLang, Perl. 
• Familiarity with ML development and deployment tools and platforms such as Databricks, MLFlow, Kubeflow Pipelines, Airflow, TensorRT or similar.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and experience deploying machine learning models using containerization technologies (e.g., Docker, Kubernetes).
• Strong understanding of software engineering principles, including version control, testing, and deployment pipelines.
• Excellent problem-solving skills and attention to detail, with the ability to analyze complex datasets and derive actionable insights.
• Effective communication and collaboration skills, with the ability to work across teams and communicate technical concepts to non-technical stakeholders.
• Experience in Agile/Scrum methodologies and working in interdisciplinary teams is a plus.
• Preferred: Experience implementing complete model lifecycle from inception to deployment to automated retraining for a complex model such as Contextual Bandit, Deep Neural Network, or similar.