Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view.
As an Wholesale Credit Risk Associate - Quantitative Research / AI-ML Engineer you will support the design, development, and operationalization of AI/ML/LLM/Gen AI solutions that deliver measurable business impact. Working under the guidance of senior engineers, you will help prepare and transform data, build and maintain scalable ETL pipelines in Python and Databricks, and contribute to fine‑tuning and evaluating generative models for domain specific tasks such as summarization and text generation. You will assist in developing and deploying microservices on cloud platforms using Docker and Kubernetes, and support real‑time monitoring to ensure model and system performance.
Job Responsibilities:
- Design, develop, and deploy state-of-the-art AI/ML/LLM/Gen AI solutions to meet business objectives.
- Develop and maintain automated pipelines for data, ensuring scalability, reliability, and efficiency.
- Implement optimization strategies to fine-tune generative models for domain specific use cases, ensuring high-quality outputs in summarization and text generation.
- Conduct thorough evaluations of generative models iterate on model architectures and implement improvements to enhance overall performance in AI/ML applications.
- Implement monitoring mechanisms to track AI/ML solutions performance in real-time and ensure model reliability.
- Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
- Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Required Qualifications, Skills and Capabilities:
- Master’s with at least 2+ years’ experience in Computer Science, Data Science, Machine Learning, or a related field.
- Proficiency in Python for model development, experimentation, and integration with the LLM APIs.
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models for optimal performance in AI/ML applications.
- Experience with cloud platforms (preferably AWS), containerization technologies (Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Proficient in building AI Agents (e.g., LangChain, LangGraph), integration of tools, and RAG-based solutions, Knowledge Graphs (e.g., neo4J).
- Experience developing end-to-end data workflows and ETL pipelines using Python and Databricks.
- Strong understanding of data modeling, normalization, and database design principles.
- Experience developing, debugging, and maintaining code in a large corporate environment using one or more modern programming languages and database querying languages.
- Ability to gather, analyze, and synthesize large, diverse data sets and to develop visualizations and reports in support of continuous improvement of applications and systems.
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.