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Metro locations, popular ml_ai title, mid-level scope, and broad GenAI requirements increase competition.
Role requires specialized ML/GenAI expertise, so cross-industry transferability is limited.
Mandatory hands-on LLM/GenAI, Python, and cloud skills imply moderate filtering of candidates.
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Design, implement, fine-tune, and optimize machine learning models including regression, classification, clustering, and optimization algorithms with a focus on GenAI applications using large language models (LLMs).
Build, deploy, and maintain GenAI AI applications leveraging LLMs for tasks such as summarization, sentiment analysis, and content generation.
Collaborate with cross-functional teams to integrate AI solutions into business processes, conduct research on emerging GenAI technologies and maintain robust data pipelines.
Strong programming skills in Python.
Hands-on experience with GenAI tools and technologies including LLMs, OpenAI APIs, LangChain, Streamlit, and vector databases.
Familiarity with prompt engineering and LLM fine-tuning techniques.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced working with large language models (LLMs) and Generative AI tools in practical deployment scenarios.
Comfortable collaborating in cross-functional teams and integrating AI-driven solutions into business workflows.
Has exposure or strong interest in cloud platforms, preferably Azure, and familiarity with emerging GenAI frameworks like LangChain and LlamaIndex.