Job Description

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

Skills Required