Machine Learning

Responsibilities: Design and implement LLM-driven features in production systems Build and maintain data pipelines for structured and unstructured data Write clean, testable Python code and maintain reusable libraries Develop prompts, tool-calling workflows, and retrieval pipelines Create evaluation suites, define success metrics, and analyze failures Diagnose and mitigate hallucination, latency, and cost issues Collaborate with product, engineering, and business stakeholders Implement monitoring, logging, and alerting for AI services Contribute to responsible-AI guardrails and human-in-the-loop processes Document designs, experiments, and findings for internal knowledge sharing

Key requirements: Bachelor in Computer Science Experience contributing to production or production-like software Strong Python programming with maintainable code Experience with web services, data integrations, testing, logging, and monitoring Hands-on experience with LLM tools/frameworks (prompting, tool-calling, retrieval) Experience evaluating LLM-powered applications, setting metrics and iterating on prompts Solid grounding in ML, statistics, and experimental design Strong communication skills with cross-functional partners Interest in responsible AI in financial services (privacy, security, human review) Familiarity with cloud deployment, containers, and modern release pipelines strong communication collaboration problem-solving Python LLM tools/frameworks prompt engineering