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Known employer and metro location increase competition, but niche GenAI requirements moderate applicant volume.
Highly specialized ML/GenAI and cloud/MLOps requirements limit cross-industry transferability.
Many explicit mandatory ML, NLP, cloud, and MLOps skills plus 7–10 years make shortlisting highly strict.
Lead design and implementation of data science models focusing on deep learning, computer vision, NLP, and generative AI applications.
Manage large-scale image and video datasets including data preprocessing, augmentation, and model fine-tuning leveraging frameworks like TensorFlow, PyTorch, and LangGraph.
Develop and optimize AI agent workflows, prompt engineering for large language models, and ensure adherence to AI security, compliance, and MLOps best practices within Azure and Databricks environments.
7–10 years overall IT experience with at least 6 years hands-on data science experience.
Bachelor’s or Master’s degree in Computer Science, Electronics & Communication, Electrical Engineering or related discipline; Master’s with specialization in AI/Deep Learning/Computer Vision preferred.
Strong Python programming skills and practical experience with deep learning frameworks such as TensorFlow and PyTorch.
Experience with NLP techniques, CNNs, reinforcement learning, transformer architectures, and cloud platforms especially Databricks and Azure.
Experienced with advanced AI technologies including transformer models (ViT), agentic AI, RAG pipelines, and generative AI workflows indicating senior expertise in AI innovation.
Comfortable working in UNIX/Linux environments with command-line scripting alongside sophisticated MLOps pipelines and enterprise AI governance.
Proven ability to design, deploy, and maintain scalable AI solutions in a cloud ecosystem (Azure/Databricks) within complex organizational contexts.