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Moderately competitive: metro location, popular Data Scientist title, and known analytics employer attract applicants.
High because the role demands specialized LLM, agent orchestration, and GenAI delivery experience not easily transferable across domains.
High because the JD mandates specialized GenAI/LLM, agentic architecture, RAG, and production governance expertise.
Lead design-to-execution delivery of enterprise-grade GenAI and agentic decisioning products, ensuring scalable and governed AI solutions.
Drive implementation of multi-agent GenAI workflows including planning, reasoning, tool-use, and orchestration across decision automation use cases.
Collaborate with AI Architects, Data Engineering, and clients to ensure solution alignment, quality delivery, and adoption, while mentoring data scientists on GenAI implementations.
Strong experience with GenAI, LLMs, and multi-agent system architectures.
Hands-on expertise in RAG, prompt engineering, and LLM orchestration frameworks.
Experience building enterprise-scale AI applications such as chatbots, copilots, or automation agents.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end delivery of agentic AI solutions with strong governance and observability focus.
Comfortable in client-facing roles driving solution adoption and alignment with AI architecture and data teams.
Proven ability to lead and mentor teams of data scientists in production-grade GenAI implementations within large enterprises.