Data Scientist Artificial Intelligence

Role Overview The role leads end-to-end delivery of AI solutions, from rapid prototyping to production deployment and scaling. You will design and build both traditional machine learning and LLM-based solutions tailored to customer needs. A core focus is owning data pipelines for training data collection, cleaning, preprocessing, and enforcing data quality/integrity. Strong Python skills and hands-on experience with deep learning frameworks (PyTorch or TensorFlow) are essential. The position partners closely with stakeholders to translate business problems into robust, reliable AI systems. Required Qualifications 6+ years of professional experience building and delivering AI/ML solutions end-to-end Expert-level Python programming for data science and production use Hands-on expertise with deep learning frameworks: PyTorch or TensorFlow Experience developing both traditional machine learning and LLM-based solutions (e.g., fine-tuning or prompt/RAG approaches) Proven experience designing and managing data pipelines: data collection, cleaning, preprocessing, validation Ability to deploy, serve, and scale models in production environments (batch and/or real-time) Strong understanding of data quality and integrity practices to ensure reliable training data Experience selecting and applying appropriate model evaluation techniques and metrics Responsibilities Prototype, develop, deploy, and scale AI solutions customized to customer requirements Design and manage end-to-end data pipelines for model training, including collection, cleaning, preprocessing, and validation Build models using traditional ML and deep learning, including LLM-based approaches where appropriate Ensure data quality/integrity and accurate labeling/processing to support reliable model training Evaluate models with appropriate metrics and validation strategies; iterate to improve performance Deploy and serve models (APIs/batch) and optimize for performance, cost, and scalability Collaborate with customers and internal teams to translate business problems into technical solutions and clear deliverables Document solutions, communicate findings, and support production monitoring and maintenance Role Overview The role leads end-to-end delivery of AI solutions, from rapid prototyping to production deployment and scaling. You will design and build both traditional machine learning and LLM-based solutions tailored to customer needs. A core focus is owning data pipelines for training data collection, cleaning, preprocessing, and enforcing data quality/integrity. Strong Python skills and hands-on experience with deep learning frameworks (PyTorch or TensorFlow) are essential. The position partners closely with stakeholders to translate business problems into robust, reliable AI systems. Required Qualifications 6+ years of professional experience building and delivering AI/ML solutions end-to-end Expert-level Python programming for data science and production use Hands-on expertise with deep learning frameworks: PyTorch or TensorFlow Experience developing both traditional machine learning and LLM-based solutions (e.g., fine-tuning or prompt/RAG approaches) Proven experience designing and managing data pipelines: data collection, cleaning, preprocessing, validation Ability to deploy, serve, and scale models in production environments (batch and/or real-time) Strong understanding of data quality and integrity practices to ensure reliable training data Experience selecting and applying appropriate model evaluation techniques and metrics Responsibilities Prototype, develop, deploy, and scale AI solutions customized to customer requirements Design and manage end-to-end data pipelines for model training, including collection, cleaning, preprocessing, and validation Build models using traditional ML and deep learning, including LLM-based approaches where appropriate Ensure data quality/integrity and accurate labeling/processing to support reliable model training Evaluate models with appropriate metrics and validation strategies; iterate to improve performance Deploy and serve models (APIs/batch) and optimize for performance, cost, and scalability Collaborate with customers and internal teams to translate business problems into technical solutions and clear deliverables Document solutions, communicate findings, and support production monitoring and maintenance