Machine Learning Engineer

Role Overview

We are looking for a skilled Machine Learning Engineer to design and implement advanced AI and machine learning solutions that can operate efficiently in production-scale environments. The position involves constructing production-grade models, establishing reliable ML workflows, and deploying AI functionalities that effectively address business challenges.

Key Responsibilities

• Create, develop, and deploy machine learning models and AI systems suitable for live production use.
• Construct and maintain robust, scalable machine learning pipelines encompassing all phases from data ingestion to deployment and ongoing monitoring.
• Work alongside various cross-functional teams to transform business requirements into AI/ML-powered solutions.
• Enhance model and system effectiveness focusing on scalability, performance, and dependability in production settings.
• Apply MLOps principles such as continuous integration/continuous delivery (CI/CD), model version control, experiment management, and automated retraining procedures.
• Track and sustain model accuracy by managing model drift and system stability over time.
• Integrate AI capabilities across areas like computer vision, natural language processing, and modern innovations including generative AI or agent-based frameworks when relevant.
• Ensure rigorous compliance with data governance rules, security protocols, and software engineering standards.

Required Qualifications and Experience

• Bachelor’s or Master's degree in Computer Science, Engineering, Data Science, or closely related disciplines.
• Minimum of 3 years' experience in machine learning engineering or AI roles.
• Proficient programming and software development abilities.
• Practical experience using machine learning and AI frameworks such as PyTorch, scikit-learn, LangChain, or similar technologies.
• Comprehensive understanding of the full machine learning pipeline including data preparation, development, testing, deployment, and monitoring processes.
• Strong grasp of software engineering best practices including testing, version control, and CI/CD methodologies.
• Well-versed with a variety of machine learning methods spanning computer vision, natural language processing, and generative AI techniques.
• Experienced in data processing and handling large-scale data infrastructure.
• Demonstrated capability in deploying machine learning models into production environments.
• Skilled in using APIs, containerization, and orchestration platforms to deploy applications.
• Familiar with major cloud platforms such as AWS, Azure, or Google Cloud Platform.