Applied AI and ML Engineer

• Contribute to the technical design of multi-agent workflows, utilizing a toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems.
• Build, prototype, and scale end-to-end AI agents. Collaborate with senior developers to build robust system architectures that prioritize reliability, usability, and auditability—ensuring clear human-in-the-loop interfaces for finance professionals.
• Take prototypes from isolated testing environments to scaled production systems. Collaborate with senior developers to design and deploy high-availability model endpoints with robust health checks, error handling, retries, and fallback mechanisms.
• Implement rigorous evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making.

Minimum qualifications:

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree in a quantitative discipline (e.g., Statistics, Engineering, Sciences).
• 1 year of experience building agentic systems.

Preferred qualifications:

• 4 years of experience using analytics to solve product problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree.
  
• Experience working in a financial, audit, or highly regulated domain where deterministic accuracy and auditability are paramount.
• Demonstrated expertise in developing and deploying AI or ML models and utilizing modern observability/monitoring tools to track performance, latency, model drift.
• Excellent communication skills, with a proven ability to translate complex technical architectures and probabilistic model behaviors.
• Strong command of classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern LLM/Generative AI tooling.

• Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
  
• 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a PhD degree in a quantitative discipline (e.g., Statistics, Engineering, Sciences).
• 1 year of experience building agentic systems.