AI Product Builder, Associate

InvestorFlow•New York, NY

3d•Hybrid

About The Position

InvestorFlow is seeking an AI Product Builder, Associate to join their AI-native product building team. This role is designed to be broad, allowing the individual to work across product discovery, AI engineering, and client-facing problem-solving alongside senior leaders. It is an opportunity for someone early in their career to learn and contribute to the conception, development, shipping, and adoption of AI-powered products at scale with real users. The role is ideal for those who want to become full-spectrum AI Product Builders rather than specialize narrowly. Candidates should be able to commute to core offices in New York, New Jersey, or San Francisco.

Requirements

Early-career engineer or technical product builder (internships, projects, or 1–3 years experience).
Hands-on exposure to LLMs, generative AI, prompt engineering, agent development
Curiosity about product delivery, not just model behavior.
Ability to explain technical and product concepts clearly to different audiences.
Technical fundamentals (i.e. Python, Go, SQL).
Interest in private markets, fintech, or complex B2B workflows (experience is a plus, not a requirement).

Nice To Haves

Python, Go, SQL, Pandas, Git, shell
Investorflow Proprietary AI Platform + Snowflake, Salesforce, LLM API (Anthropic, OpenAI, and others), MCP-compatible integrations
Kubernetes (basic familiarity), data pipelines, ETL
Github, Jira, Productboard

Responsibilities

Analyze private markets workflows and user behavior to identify high-impact problems worth solving.
Synthesize client feedback, usage signals, and business context into clear product hypotheses and feature definitions.
Contribute to roadmap definition, feature scoping, and prioritization alongside senior PMs and engineers.
Influence product decisions through evidence, judgment, and execution quality.
Use hands-on technical work to validate product ideas and de-risk delivery.
Design, implement, and iterate on AI-powered product features where engineering is required to prove value.
Build and refine LLM prompts, pipelines, and workflows that surface insights and perform tasks.
Collaborate closely with engineers to take validated concepts into production.
Monitor AI skill quality signals across clients and channels; tune prompts, adjust data contexts, and validate improvements using structured evaluation approaches.
Help produce documentation, release notes, and demos that explain what was built and why.
Support internal teams (product, engineering, client success) in understanding and deploying new features.
Occasionally engage directly with clients or end users to understand real-world usage and pain points.