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Metro-based mid-level ML role with common requirements increases candidate competition.
Core ML, Python, and MLOps skills are broadly transferable across industries.
Explicit 3-5 years plus many mandatory ML, MLOps, cloud, and containerization skills.
Develop, implement, deploy, and maintain AI/ML and deep learning solutions using frameworks like TensorFlow, Scikit-learn, and PySpark.
Handle preprocessing and analysis of large datasets to extract actionable insights and optimize model performance in production.
Manage deployment pipelines including Flask/FastAPI applications, MLOps practices (MLFlow, CI/CD), and containerization using Docker and Kubernetes.
3-5 years of work experience in data science or related AI/ML role.
Strong programming skills in Python and experience with machine learning frameworks (TensorFlow, Scikit-learn, PySpark).
Experience with cloud platforms, preferably AWS; knowledge of MLOps tools and container orchestration (Docker, Kubernetes).
Location requirement: Pune, India.
Has significant hands-on experience deploying and optimizing scalable AI/ML solutions with full lifecycle responsibility including MLOps and containerization.
Comfortable working with cross-functional teams and communicating technical insights effectively.
Exposure to emerging technologies like Generative AI/LLMs and cybersecurity domains adds competitive advantage.