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Popular mid-level ML role in a metro with 3–6 years requirement increases candidate competition.
High domain specificity due to ML, deep learning, and MLOps skill requirements.
Explicit 3–6 years plus multiple mandatory ML/MLOps technologies makes filtering stringent.
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Design, develop, train, and deploy end-to-end machine learning and deep learning models and pipelines for business use cases.
Build scalable, production-ready ML solutions including data preprocessing, model training, evaluation, hyperparameter tuning, and deployment using MLflow.
Monitor and optimize deployed models' performance over time, ensuring lifecycle management and collaborating with cross-functional teams for AI solutions.
3-6 years of hands-on experience in machine learning and deep learning model development and deployment.
Strong proficiency in Python and SQL, including complex queries and performance optimization.
Experience with MLflow for experiment tracking, model versioning, and deployment.
Postgraduate or equivalent education (explicitly mentioned) and availability in Pune with hybrid working model.
Experienced ML engineer capable of owning end-to-end ML projects including pipeline automation and lifecycle management.
Proficient with modern ML frameworks (PyTorch/TensorFlow) and ML ops practices including containerization and CI/CD.
Familiar with advanced ML concepts such as Transformer architectures and exposure to Generative AI and model monitoring tools for production-grade solutions.