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Metro location and common data-science title increase competition, but GenAI specialization and senior level moderate density.
Specialized GenAI and deployment skills are required but remain reasonably transferable across industries.
Mandatory hands-on Python and GenAI skills plus broad LLM, CV, cloud, and deployment requirements enforce strict filters.
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Design and implement advanced solutions using Large Language Models (LLMs) including end-to-end development ownership.
Develop and maintain code libraries, tools, and frameworks to support generative AI and participate across the entire software development lifecycle.
Collaborate with cross-functional teams to align product roadmaps and integrate AI capabilities into core systems.
Mandatory hands-on experience with Python and core Generative AI skills including NLP, computer vision, and generative AI frameworks.
Experience with SaaS LLM platforms such as Azure OpenAI, Google Vertex AI, AWS Bedrock, and open-source tools like TensorFlow/PyTorch and Huggingface.
Proficiency in cloud platforms (Azure, AWS, GCP); cloud certification preferred but not mandatory.
Educational qualification: B.E/B.Tech/M.Tech in Computer Science or related field, or equivalent experience. Work Experience Required: Not explicitly mentioned in the JD.
Strong expertise in NLP use cases like classification, summarization, Q&A, chatbots, and document AI, plus computer vision and audio analysis skills.
Experienced in prompt engineering techniques (COT, TOT, ReAct), LLM fine-tuning (PEFT, RLHF) and GPU utilization for generative AI model development.
Comfortable working independently with end-to-end ownership, and collaborating in cross-functional, product-focused environments to evolve AI solutions.