Must have two (2) years of experience with: independently architecting and developing end-to-end AI or machine learning applications, including translating ambiguous business requirements into technical specifications; designing UI/UX workflows and dashboards using tools (Pencil, Tableau or similar), building interactive web application prototypes using Streamlit, and engineering scalable back-end model-serving infrastructure; leading AI product lifecycle from ideation to deployment, including defining technical roadmaps, prioritizing AI/ML feature backlogs using data-driven frameworks, and coordinating iterative development sprints across engineering, data science, and business teams using JIRA; applying Natural Language Processing (NLP) and Deep Learning methodologies utilizing Transformer architectures, Transfer Learning, and Semantic Search/Information Retrieval, using Python libraries including NLTK, gensim, and spaCy; applying Advanced Modeling & Statistical Inference methodologies to develop predictive models using Gradient Boosting frameworks (XGBoost, LightGBM, CatBoost), Bayesian statistical modeling (PyMC), and Graph Neural Networks (PyTorch Geometric); applying Reinforcement Learning methodologies to design and optimize autonomous decision-making systems, including developing custom simulation environments using Gymnasium and executing distributed training workflows using Ray; hands-on development using Python (Scikit-learn, PyTorch, Tensorflow) to build AI solutions, including integrating external data and services via APIs including Google Suite APIs; utilizing NoSQL or high-dimensional data stores and performing extensive SQL database management, utilizing multiple SQL dialects, specifically PostgreSQL, PL/SQL (Oracle), and Spark SQL to query complex datasets for AI solutions; operationalizing and scaling machine learning models through automated pipelines and model versioning using GitLab or GitHub and open-source frameworks (Kubeflow/MLflow), including utilizing containerization tools (Docker or Podman) to deploy models on container orchestration platforms including Kubernetes and OpenShift; communicating and presenting complex AI concepts, model performance, and product value to both technical (engineering) and non-technical (executive) audiences using data visualization platforms including Tableau; developing data science and AI solutions within a B2B technology marketing or software industry context; and researching, evaluating, and prototyping novel AI methodologies, including new model architectures from academic papers, emerging Deep Learning frameworks, and advanced information retrieval technologies, and integrating them into production-level business solutions. *Telecommuting role to be performed anywhere in the U.S. Analyze and process large-scale structured and unstructured datasets using SQL tools (PostgreSQL, PL/SQL, Spark SQL), API integrations (including Google Suite APIs), and automated preprocessing workflows to prepare data for advanced statistical and machine learning model development.