Applied AI/ML Engineer

• Lead the technical design of multi-agent workflows, utilizing a various toolkit (ML and Gemini LLMs) to solve complex, multi-layered financial problems.
  
• Build, prototype, and scale end-to-end AI agents. Outline 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. Design and deploy high-availability model endpoints with health checks, error handling, retries, and fallback mechanisms.
  
• Implement evaluation frameworks and guardrails to eliminate logical errors, hallucinations, and biases in automated financial decision-making.
  
• Partner closely with Product Managers, Engineers, and Finance stakeholders to translate ambiguous finance problems into concrete technical specification. Act as a self-sustaining technical leader who helps unblock system integration hurdles in partnership with Engineering teams.
  

Minimum qualifications:

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

Preferred qualifications:

• 8 years of experience in full-stack development for end-to-end machine learning solutions.
• Experience building Agentic tools and systems (production-ready, not POCs).
• Experience building autonomous or semi-autonomous agents with governance, logging, and human-in-loop flows.
• Experience in classical ML modeling (e.g., time-series forecasting, tree-based models) alongside modern Large Language Model (LLM)/Generative AI tooling.
• Demonstrated expertise in developing and deploying AI or ML models and utilizing modern observability/monitoring tools to track performance, latency, and model drift.
• Excellent communication and storytelling skills, with an ability to translate complex technical architectures and probabilistic model behaviors to executive finance leadership.

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