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