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 trainingdata 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