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Mid-level ML engineer in Mumbai with broad ML/MLOps requirements increases candidate competition.
Core ML engineering skills are transferable, but consulting and productionization needs modestly raise domain specificity.
Explicit 5+ years requirement plus mandatory ML, deployment, and cloud skills tightens shortlisting.
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.
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.
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.