Analyst within Credit Risk Management team identifying credit segmentation opportunities using statistical methods. Collaborating with teams to enhance credit decision process and policies.
About the role
- Design and implement LLM-powered solutions for enterprise-scale use cases requiring structured outputs and domain adaptation.
- Develop and maintain AI agents capable of handling domain-specific knowledge and workflows in production.
- Apply prompt engineering, retrieval-augmented generation (RAG), and other techniques to embed company expertise into LLM pipelines.
- Contribute to LLM continuous pretraining and fine-tuning efforts to adapt models to specialized tasks.
- Integrate LLM-based agents into production environments with a focus on scalability, latency, reliability, and compliance.
- Conduct experiments to evaluate model quality, effectiveness, and business impact.
- Establish monitoring, guardrails, and evaluation frameworks for safe and effective GenAI usage.
- Stay current with research and developments in LLMs, agent architectures, orchestration frameworks, and generative AI platforms.
- Use predictive modeling to enhance customer experience, revenue generation, dynamic pricing, and demand forecasting.
- Communicate complex findings and insights to both technical and non-technical audiences.
- Collaborate with different functional teams to integrate models into business operations.
Requirements
- Strong understanding of LLMs, GenAI, and AI agent architectures.
- Solid understanding of fine-tuning and continuous pretraining approaches (experience is a strong plus).
- Proven expertise in prompt engineering and methods for grounding LLMs with domain-specific data.
- Demonstrated experience in deploying AI agents into production, including integration with APIs, databases, and business systems.
- Expertise in machine learning algorithms such as regression, classification, clustering (GLM, Random Forest, Gradient Boosting, deep learning, etc.).
- Solid understanding of statistical techniques (regression, distributions, hypothesis testing).
- Proficiency in Python and familiarity with ML/AI frameworks (e.g., PyTorch, TensorFlow).
- Strong problem-solving skills and ability to translate business requirements into GenAI or traditional ML solutions.
- Excellent communication skills for cross-functional collaboration and for explaining technical concepts to non-technical audiences.
- Fluent English: Interviews will be held in this language.
Benefits
- Work at the intersection of data science and aviation
- Opportunity to work with large, complex datasets
- Apply state-of-the-art machine learning techniques
- Collaborate with a highly skilled team of data scientists, engineers, and business leaders
Job title
Job type
Experience level
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Not specified
Degree requirement
Tech skills
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