Senior AI Engineer

Role Overview

We’re looking for a Senior AI Engineer who can design, build, and operate the production infrastructure behind our AI-driven financial products. This is not a research or prototyping role—you’ll own the full lifecycle of agentic systems and data pipelines that power real trading decisions, from experiment to deployment to monitoring.

You’ll work at the intersection of backend engineering, data infrastructure, and applied AI, building autonomous agents that execute business workflows, reliable RAG pipelines for financial data, and the cloud infrastructure that keeps it all running in production. If you care about stability, reliability, and shipping systems that actually work at scale, this role is for you.

Key Responsibilities

Agentic Systems & AI Infrastructure

• Design, build, and maintain autonomous AI agents that execute end-to-end business workflows in financial operations

• Develop and optimize RAG pipelines—chunking strategies, embedding models, vector search, and retrieval orchestration—for financial documents and market data

• Build and maintain tool repositories and MCP integrations for internal agentic systems

• Implement AI coding workflows and LLM-powered automation to accelerate internal development velocity

Backend & Data Pipelines

• Build and maintain robust data pipelines for both structured market data and unstructured sources (earnings reports, news, filings, alternative data)

• Design and operate APIs for model serving, data access, and system integrations

• Work with relational databases, distributed data systems, and vector stores to support AI and analytics workloads

• Ensure data quality, lineage, and observability across all pipelines

Cloud Infrastructure & Operations

• Deploy and manage services on AWS (EC2, RDS, OpenSearch, S3, Lambda, and related services)

• Implement monitoring, logging, and alerting for production AI systems

• Own end-to-end operational workflows: experiment → deploy → monitor → iterate

• Design for stability, reliability, and compliance—not chasing experimental architectures

Cross-Functional Collaboration

• Work closely with engineers, product managers, and investment teams to translate requirements into production systems

• Take ownership of projects from conception through deployment and ongoing operations

• Document technical decisions, system architectures, and operational runbooks

Requirements

Required Experience & Skills

• 5 to 8 years of backend engineering experience in Python, with a strong track record of building production systems

• Proven experience building and maintaining data pipelines for both structured and unstructured data

• Solid understanding of relational databases and distributed data systems

• Working knowledge of multi-agent orchestration workflow and RAG

• Hands-on experience with AI coding tools, agentic systems, and autonomous agents that execute business workflows

• Experience deploying and operating services on cloud platforms (AWS preferred: EC2, RDS, OpenSearch, S3, Lambda)

• Comfortable owning end-to-end workflows from experimentation through deployment and monitoring

• Proficiency in API development and system integration

• Strong problem-solving skills with meticulous attention to detail

• Excellent communication skills to work effectively across engineering, product, and investment teams

Strongly Preferred

• Experience in fintech, quantitative finance, or financial services

• Knowledge of options mechanics, equities, or currency markets

• Understanding of financial data sources, market data feeds, and regulatory compliance requirements

• Track record of designing systems for stability and reliability over novelty

Personal Attributes

Builder mentality—you ship working systems, not slide decks

Production-first mindset—you design for uptime, not demos

High ownership—comfortable taking projects from idea to production with minimal hand-holding

Pragmatic about AI—you know when to use a simple script vs. a multi-agent framework

Curious about markets—genuine interest in financial markets and how technology creates edge

Team player—can bridge technical depth with business context across stakeholders