The Senior Data Scientist / AI Engineer is responsible for developing AI and machine learning models and pipelines, with a strong focus on generative AI, large language models (LLMs), and predictive modeling. This role involves close collaboration with business and product stakeholders to understand data requirements and inform product design and decision-making. It also includes working with software engineers to implement and deploy solutions.

The role may require quick adoption of new tools and technologies, supported by a strong foundation in machine learning/AI and fundamental programming skills.

Key Responsibilities

Design, develop, and maintain systems to process large datasets.
Apply machine learning and AI frameworks to solve complex problems, with a focus on deep learning, regression, classification, and clustering.
Build and evaluate LLM-based solutions, including retrieval-augmented generation pipelines.
Collaborate with stakeholders to collect and transform data for analysis.
Automate data collection and build experimental frameworks.
Explore and analyze datasets to extract actionable insights.
Create business reports and presentations based on findings.
Develop and maintain data models, interfaces, and integrations to support evolving business needs.
Contribute to strategic planning of AI initiatives.

Qualifications

Advanced degree (PhD or MSc) in Computer Science, Machine Learning, AI, or a related field, or equivalent practical experience.
3+ years of experience in a data science or machine learning role.
Proficient in machine learning tools (e.g., PyTorch, TensorFlow, Scikit-learn, XGBoost).
Solid understanding of statistical analysis and machine learning algorithms.
Strong programming skills in languages such as Python, R, or SQL.
Experience designing, testing, and deploying machine learning models.
Knowledge of LLM development and evaluation, including prompting techniques and retrieval-augmented generation.
Familiarity with tools and libraries in the LLM ecosystem (e.g., langchain, vector databases, orchestration frameworks).
Skilled in accessing and managing data via SQL, cloud-based data pipelines, or Big Data platforms (e.g., Spark, Hadoop).
Understanding of software development best practices, including CI/CD, testing, and code reviews.
Awareness of MLOps practices and cloud-based compute/orchestration platforms.
Industry experience in finance, revenue cycle management, or similar domains is a plus, but not required.
Strong communication skills and the ability to work effectively in both individual and collaborative settings.