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Tier-1 brand, mid-level ML role, and metro location increase applicant competition.
Role requires specialist ML/GenAI and MLOps expertise, limiting cross-industry transferability.
Multiple mandatory ML, GenAI, MLOps, and cloud technology requirements increase screening strictness.
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Design, develop, deploy, and monitor end-to-end machine learning and NLP models including GenAI solutions using Python and ML frameworks.
Lead model productionisation and guide development teams on ML use case implementation and deployment best practices.
Implement Responsible AI principles and collaborate with data scientists and engineers on model lifecycle including performance monitoring and retraining.
Strong proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Experience designing, developing, and deploying ML and NLP models, including GenAI solutions and production-scale applications.
Hands-on experience with cloud-based AI services from Azure, AWS, or GCP and ML lifecycle tools like Azure ML or AWS SageMaker.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in applying advanced ML techniques including supervised, unsupervised, reinforcement learning, and fine-tuning large language models (LLMs) using LoRA or QLoRA.
Capable of integrating GenAI capabilities into enterprise applications, preferably with experience in Microsoft Copilot Studio.
Familiar with MLOps practices, containerization (Docker, Kubernetes), and Python web frameworks (Flask, Django), indicating ability to handle end-to-end AI/ML solution architecture and deployment.