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Mid-level GenAI Data Scientist title with broad skillset but non-metro location yields moderate competition.
Core ML/GenAI skills are transferable across industries, but required cloud and production integration increases domain specificity.
Explicit 5+ years, 2+ years GenAI experience, and specific tech stack and cloud requirements enforce strict filters.
Design and deliver scalable AI architectures and solutions including GenAI, Agentic AI, and traditional ML across multiple business units.
Lead end-to-end AI/ML projects from feasibility analysis, model development, to deployment on hybrid/cloud environments (Azure, AWS).
Support business units in adopting and maintaining AI solutions, mentor junior members, and drive AI best practices within the organization.
Bachelor's degree in computer science, engineering, or related discipline (advanced degree preferred but not mandatory).
Minimum 5 years of AI/ML experience, with at least 2 years specializing in GenAI, LLMs, RAG, embeddings, prompt engineering, agent orchestration.
Proficiency in Python programming and experience with ML frameworks like TensorFlow, PyTorch, and Keras.
Experience building scalable AI applications on Azure and AWS cloud platforms; familiarity with full-stack development and CI/CD processes.
Experienced in translating complex business requirements into scalable AI/ML solutions deployed at production scale across varied domains.
Capable of leading AI strategy, mentoring teams, and evangelizing emerging AI technologies to ensure adoption and governance.
Comfortable working in a hybrid cloud environment with strong technical skills across AI/ML, cloud platforms, and software development lifecycle.