15h Left: Machine Learning Engineer

16 hours ago


Bengaluru, India Weekday AI Full time

This role is for one of the Weekday's clients Min Experience: 3 years Location: Bangalore JobType: full-time We are seeking a highly skilled Machine Learning Engineer to design, build, and scale production-ready ML systems. The ideal candidate will have a strong software engineering background, hands-on experience with ML frameworks, and a deep understanding of MLOps principles. You will be responsible for architecting model training, inference, and deployment pipelines that enable efficient experimentation and reliable performance at scale. Requirements What You’ll Do - Design and implement automated training and inference pipelines, including building a model registry system for artifact tracking, versioning, and lineage. - Develop frameworks for on-demand model training and architect parallel processing systems to support inference in event-driven environments. - Design and develop robust APIs that expose machine learning capabilities for internal and external use. - Build ETL pipelines and preprocessing frameworks tailored for ML applications. - Implement comprehensive monitoring solutions to identify performance bottlenecks and optimize system scalability. - Create infrastructure to support offline and online experimentation. - Build internal tools and frameworks that standardize ML workflows across teams and improve efficiency. - Stay up to date with emerging trends in machine learning engineering and MLOps, and evaluate new tools and best practices. - Contribute to technical architecture and decision-making to enhance ML infrastructure and platform capabilities. Required Qualifications - Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - 3+ years of experience in software engineering with a focus on machine learning systems. - Strong programming skills in Python and proficiency with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. - Hands-on experience building and maintaining production-grade ML pipelines. - Proficiency with containerization tools like Docker and Kubernetes. - Experience working with cloud platforms (AWS, GCP, or Azure) and their ML services. - Strong understanding of software engineering best practices, including version control, CI/CD, and testing. - Experience with data processing frameworks for large-scale data workflows. - Excellent problem-solving skills and the ability to work independently on complex technical challenges. - Strong communication and collaboration skills to work effectively with cross-functional teams. Key Skills Machine Learning | Python | TensorFlow | PyTorch | Scikit-learn | MLOps | Docker | Kubernetes | Cloud Platforms (AWS/GCP/Azure) | CI/CD | ETL Pipelines



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