Role Overview

As a Machine Learning Engineer, you will collaborate closely with data scientists, software engineers, and business stakeholders to align machine learning solutions with company objectives. You will be responsible for designing, developing, and deploying machine learning models that tackle real-world challenges across diverse domains, including recommendation systems, natural language processing (NLP), and predictive analytics.

Key Responsibilities

• Work with cross-functional teams to understand business goals and translate them into effective machine learning solutions.
• Design, develop, and implement advanced ML models and algorithms to solve complex business challenges.
• Deploy machine learning models into production environments, ensuring scalability, reliability, and maintainability in collaboration with engineering teams.
• Continuously monitor model performance in production, proactively identifying and resolving issues to optimize accuracy and efficiency.
• Stay up to date with emerging ML research and technologies, exploring new methodologies to enhance model performance.
• Document methodologies, processes, and findings, fostering knowledge-sharing and collaboration across teams.

Required Skills & Experience

• Education: Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (Master’s or Ph.D. preferred).
• Experience: Minimum of 3 years of experience in machine learning engineering or related fields, with a proven track record of deploying ML models into production.
• Programming: Proficiency in Python, GoLang, and Perl, with strong debugging skills.
• ML Tools & Platforms: Hands-on experience with Databricks, MLflow, Kubeflow Pipelines, Airflow, TensorRT, or similar ML development and deployment tools.
• Data & Model Development: Expertise in data preprocessing, feature engineering, and model evaluation techniques.
• Cloud & Deployment: Familiarity with cloud platforms (AWS, Azure, GCP) and experience deploying ML models using containerization technologies (Docker, Kubernetes).
• Software Engineering Principles: Strong understanding of version control, testing, and deployment pipelines.
• Problem-Solving & Collaboration: Excellent analytical skills with a keen attention to detail and the ability to derive actionable insights from complex datasets.