Design and implement GenAI -powered solutions using frameworks like <\/span><\/span>LangChain<\/span>, Hugging Face, and Azure AI Studio<\/span>. <\/span><\/span> <\/span> Build agentic workflows for autonomous task execution using <\/span><\/span>LangGraph<\/span>, OpenAI Agents SDK, and Databricks Genie<\/span>. <\/span><\/span> <\/span> Collaborate with cross -functional teams to translate business needs into AI applications with measurable impact<\/span>. <\/span><\/span> <\/span> Develop and <\/span><\/span>maintain<\/span> <\/span>data pipelines and model deployment workflows using Databricks, Snowflake, and Azure Data Factory<\/span>. <\/span><\/span> <\/span> Contribute to model evaluation, prompt engineering, and fine -tuning of LLMs for enterprise use cases<\/span>. <\/span><\/span> <\/span> Ensure responsible AI practices through observability, governance, and feedback loops.<\/span><\/span> <\/span> Experience in Test Driven Development and model evaluation strategies, including offline and online evaluation pipelines.<\/span><\/span> <\/span> Prior work experience in an agile, cross -functional team, collaborating with data scientists, engineers, and product managers.<\/span><\/span> <\/span> <\/span> Ability to break down complex AI problems, estimate development effort, and deliver scalable solutions.<\/span><\/span> <\/span> Up -to -date knowledge of GenAI and LLM trends, including frameworks like Gemini, <\/span><\/span>LlamaIndex<\/span>, and <\/span><\/span>LangGraph<\/span>, and their enterprise applications.<\/span><\/span> <\/span> Understanding of AI governance, including model explainability, fairness, and security (e.g., prompt injection, data leakage mitigation).<\/span><\/span> <\/span>
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