Machine Learning

• Design and implement LLM‑driven features in production systems.
• Build and maintain data pipelines for both 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.

• Bachelor’s degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
• Experience contributing to production or production‑like software through work, internships, research, open source, or substantial personal projects.
• Strong programming ability in Python with clear, tested, and maintainable code.
• Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
• Hands‑on experience building with LLM tools or frameworks (prompting, structured outputs, tool‑calling, retrieval, multi‑step workflows) and awareness of common failure modes.
• Experience evaluating LLM‑powered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
• Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
• Strong communication skills and comfort working with product, engineering, and business partners.
• Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
• Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000