ML Engineer
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Summary
Builds and maintains production machine learning pipelines and MLOps infrastructure for a global data science team: deploying, monitoring, and optimizing models (image, text, audio, tabular) for latency, memory, and throughput, with CI/CD and containerization. Core stack: Python, PyTorch/TensorFlow, MLflow/DVC, Azure ML, Docker, Grafana.
About the Role
The ML Engineer works within a global team of Data Scientists, delivering machine learning methods into production in the most efficient way possible. This role designs, develops, and maintains ML pipelines, implements monitoring and alert systems, and optimizes trained AI models for latency, memory, and throughput. Based in Hyderabad, Noida, or remote, the ideal candidate has in-depth knowledge of model management, experiment tracking, and MLOps practices.
Key Responsibilities
• Design, develop, and maintain ML pipelines, ensuring high reliability and scalability.
• Implement scalable machine learning infrastructure to deploy algorithms based on image, text, audio, and tabular data.
• Optimize trained AI models for latency, memory, and throughput.
• Develop CI/CD pipelines and containerize production applications.
• Automate existing workflows within the Data Science team.
• Monitor and troubleshoot production ML systems using alert systems.
• Collaborate with cross-functional teams across data and engineering to solve complex problem statements.
• Lead research initiatives to explore new methodologies and contribute to best MLOps practices.
Key Skills
• 2-5 years of experience, with a Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Strong proficiency in Python, with extensive experience in PyTorch or TensorFlow.
• Extensive knowledge of key machine learning metrics and optimization techniques.
• Strong knowledge of version control systems (Git, data versioning).
• Experience with model management and experiment tracking frameworks (e.g., MLflow, DVC, LakeFS).
• Strong knowledge and practical understanding of MLOps techniques.
• Prior experience deploying and monitoring scalable ML models, CI/CD pipelines, and containerization.
• Excellent problem-solving, analytical, and communication skills.
Good to Have
• Extensive experience with the Azure ecosystem, particularly Azure Machine Learning.
• Experience developing computer vision, text, audio, or tabular data models.
• Strong proficiency in Gitlab CI, Jenkins, Grafana, and Docker.
• Excellent software engineering skills in API design and concurrency.