We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Quant Analytics Associate within our Business Banking Data and Analytics Team, you will be instrumental in constructing predictive models and developing robust RAG pipelines. Collaborating closely with cross-functional teams, you will extract valuable insights from complex datasets to promote data-driven decision-making across the organization. Your focus will include addressing problem statements and innovating solutions for complex challenges. Additionally, you will build AI-based solutions to enhance both technological and business efficiency.

Job Responsibilities:

• Align ML problem definition with business objectives to ensure solutions address real-world needs.

• Design, develop, and manage prompt-based models on Large Language Models (LLMs) for complex financial services tasks.

• Architect and oversee the development of next-generation machine learning models and systems using cutting-edge technologies.

• Drive innovation in machine learning solutions, focusing on scalability, flexibility, and future-proofing.

• Promote software and model quality, integrity, and security throughout the organization.

• Architect and implement scalable AI Agents, Agentic Workflows, and GenAI applications for enterprise deployment.

• Integrate GenAI solutions with enterprise platforms using API-based methods.

• Establish validation procedures with Evaluation Frameworks, bias mitigation, safety protocols, and guardrails.

• Collaborate with technology teams to lead the design and delivery of GenAI products.

Required Qualifications, Capabilities, and Skills

• Formal training or certification in software engineering concepts and 8+ years of applied AI/ML experience.

• Strong understanding of the Software Development Life Cycle (SDLC), Data Structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, and Statistics.

• Experience with cloud platforms such as AWS, GCP, or Azure.

• Proficiency in RDBMS, NoSQL databases, and prompt design.

• Demonstrated expertise in machine learning frameworks such as Tensor Flow, Py Torch, pyG, Keras, MXNet, and Scikit-Learn.

• Proficient in building AI Agents (e.g., Lang Chain, Lang Graph, Auto Gen), integration of tools (e.g., API), and RAG-based solutions (e.g., open search), Knowledge Graphs(e.g., neo4J).

• Proven track record of building and scaling software and/or machine learning platforms in high-growth or enterprise environments.

• Exceptional ability to communicate complex technical concepts to both technical and non-technical audiences.