Machine Learning Engineer

WHAT YOU WILL DO

• Work closely with data scientists, software engineers, and business stakeholders to understand requirements and translate them 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.
• Collect, preprocess, and analyze data to extract meaningful insights and features that drive model development and optimization.
• Evaluate and benchmark machine learning models using appropriate metrics and techniques, iterating on designs to improve performance and robustness.
• Deploy machine learning models into production environments, collaborating with software engineering teams to ensure scalability, reliability, and maintainability.
• Experience with developing machine learning models at scale from inception to business impact
• 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.
• Collaborate with stakeholders to understand business goals and objectives, communicating findings and recommendations to drive strategic decision-making and business impact.

WHO YOU ARE

• 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, and some knowledge in Tensorflow and model deployment tools like Airflow, Databricks, AWS, Docker. 
• 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.