Praxis Global Alliance - Machine Learning engineer
Praxis Global Alliance (company site, opens in a new tab)5+ yrsMumbaiOn-siteDirect career pageEst. 12–24 LPA
Posted on 8 Sept
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Job description
Structured overview of role & requirements
About This Role
- Design, develop, and deploy scalable machine learning models and maintain end-to-end ML pipelines from data ingestion to production monitoring.
- Collaborate with cross-functional teams including product, data, and engineering to apply ML solutions to complex business problems.
- Own model optimization, lifecycle management, and adherence to ML engineering best practices to ensure performance and reliability in production environments.
Minimum Requirements
- 5+ years of experience in machine learning engineering or similar roles.
- Bachelor’s or master’s degree in computer science, engineering, or related field.
- Strong programming skills in Python with hands-on experience in TensorFlow, PyTorch, or Scikit-learn.
- Proven experience deploying ML models into production and familiarity with cloud platforms such as AWS, GCP, or Azure.
Ideal Candidate Profile
- Experienced in building and managing production-grade ML systems with end-to-end pipeline ownership reflecting operating experience in production environments.
- Comfortable working in collaborative, cross-functional teams including product and engineering stakeholders to solve business problems through ML.
- Familiar with modern MLOps practices including containerization, orchestration tools (Docker, Kubernetes), and CI/CD pipelines to support model deployment and monitoring.