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Metro location and mid-level ML role with broad skill demands create moderate competition.
Core ML model development and deployment skills are transferable, though generative-model specialization reduces portability.
Requires specific ML frameworks and generative-model expertise but no explicit years, making filters moderately strict.
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Design, build, and train AI and generative AI models including NLP, computer vision, and predictive analytics algorithms.
Integrate AI functionalities into software systems including developing APIs and interfaces for AI-powered features.
Handle large datasets for cleaning, preprocessing, feature engineering, optimize models for performance, and deploy AI solutions to production.
Proficiency in Python; knowledge of Java or C++ is a plus.
Experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
Hands-on experience with generative AI models like GPT, VAE, GANs, and Diffusion Models.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end AI development lifecycle from data handling to deployment in production environments.
Strong technical expertise in machine learning, deep learning, and generative AI model implementation.
Capable of collaborating with cross-functional teams and translating technical AI concepts to non-technical stakeholders.