TradeStation is an online brokerage firm focused on delivering an ultimate trading experience. They are seeking a Senior Product Manager of AI Products & Platforms to own the product lifecycle for AI-powered features and platforms, bridging strategy and execution while collaborating across various teams.
Responsibilities:
- Own the AI product lifecycle — define roadmaps, write requirements, and deliver AI-powered features from concept through launch
- Apply spec-driven development practices — use LLMs and MCPs to create clear, testable specifications and living documentation; contribute to refining SDD workflows across Product and Engineering
- Design experiments and triage POCs — validate concepts through testing and rapid prototyping (no-code/low-code tools), then scale winners into production
- Partner with engineering to configure and operate AI platforms — LLMs, agent frameworks, and semantic pipelines with monitoring, guardrails, and incident readiness
- Drive continuous improvement — track adoption, quality, drift, and cost/latency; define requirements for model retrains and platform optimizations
- Implement responsible AI guardrails and navigate SEC/FINRA requirements — partner with InfoSec, Legal, and Compliance to create repeatable approval playbooks
- Influence enterprise adoption and report measurable outcomes — ensure platform capabilities are leveraged across business units and communicate progress tied to business impact
- Stay ahead of emerging AI technologies and industry best practices
Requirements:
- Bachelor's degree in business, finance, or technical field
- 7+ years in product/platform roles, including 1+ years building or operating AI/ML-powered products or large-scale workflow automations in regulated environments
- hands-on experience using Claude (or similar LLMs) with GitHub and Model Context Protocol (MCP) for spec-driven development and living documentation; eager to adopt emerging PM workflows and share knowledge with peers
- expert at building roadmaps, writing clear requirements and acceptance criteria, defining KPIs (adoption, ROI, latency/SLOs), and driving iterative delivery in agile environments
- deep understanding of LLM architectures, agent frameworks, prompt engineering, and semantic search; able to prototype AI workflows using no-code/low-code tools and translate technical constraints into product decisions
- hands-on experience designing A/B tests, causal experiments, and rapidly assessing technical/business viability of proofs-of-concept
- proven ability to align AI initiatives with SEC/FINRA requirements and partner effectively with InfoSec, Legal, and Compliance to accelerate approvals
- skilled at influencing without authority; ability to partner across Product, Data Science, Enterprise Data, Engineering, and Operations teams
- experience integrating AI platforms with enterprise data ecosystems (e.g., Snowflake, Databricks, data lakes)
- Demonstrated experience embedding AI into customer experiences and internal operations with measurable KPIs
- understanding of equities, options, futures, and brokerage workflows (back office, middle office, risk, compliance)
- Familiarity with enterprise data platforms and AI tooling in Databricks or Snowflake preferred
- Advanced degree in a relevant field (e.g., Computer Science, Financial Engineering, Data Science, MBA)