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Tier-1 brand and metro location increase applicant density, but senior, specialized AI skillset reduces generalist competition.
Core ML engineering and MLOps skills transfer across industries, though financial-domain knowledge is helpful.
Multiple mandatory ML, GenAI, MLOps and cloud skills imply moderately strict technical filtering.
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Design, develop, and deploy AI/ML models including NLP, predictive analytics, and GenAI to enhance product features and operational efficiency.
Collaborate within a cross-functional squad to integrate AI capabilities into products with scalable and reliable deployment pipelines.
Manage end-to-end ML model lifecycle including data ingestion, training, evaluation, deployment, monitoring, and continuous improvement.
Strong experience in Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
Experience with GenAI/LLMs including prompt engineering and RAG architectures.
Proven track record of delivering AI solutions in production environments with familiarity in cloud platforms (Azure preferred).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in agile, squad-based product delivery environments collaborating across engineering, product, and business teams.
Proficient in MLOps practices including CI/CD, model deployment, and monitoring to ensure scalable AI solutions.
Demonstrates a pragmatic approach focused on business impact and continuous learning of emerging AI technologies.