Back
Tier-1 brand, mid-level generalist ML role in a metro with broad cloud/ML requirements increases competition.
Strong ML and cloud skill requirements mean moderate transferability across industries.
Explicit 4–7 years plus mandatory ML and cloud skills and degree requirements make filters stringent.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop, train, and deploy machine learning models for use cases including prediction, recommendation, NLP, and computer vision.
Design and implement scalable ML pipelines on cloud platforms (preferably AWS, also Azure) incorporating AI/ML services and containerization tools.
Analyze data and KPIs to formulate data science problems and provide impactful AI-driven solutions integrated into enterprise applications.
4 to 7 years of relevant experience in AI/ML development and deployment.
Proficiency in Python/PySpark and machine learning libraries like scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face Transformers.
Experience with cloud ML services (AWS preferred) including SageMaker, Lambda, Step Functions, API Gateway, CloudWatch, and containerization (Docker, Kubernetes).
Educational qualifications: Bachelor of Technology and/or Master of Business Administration/MCA.
Experienced in building end-to-end ML solutions including data preprocessing, feature engineering, model optimization, and deployment in cloud environments.
Strong knowledge of a variety of ML techniques including supervised, unsupervised, reinforcement learning, and deep learning architectures (CNNs, RNNs, Transformers).
Able to design and deploy production-level ML pipelines using MLOps frameworks and integrate ML models within enterprise workflows.