Founding AI Engineer - Build Core Data Pipelines (Equity)

TalentProof; New York (NY); On-site; USD 110,000 - 160,000; Full time

Benefits offered by this job

Equity

Medical

Flexible time off

Mac

Job summary

TalentProof is seeking a founding Applied-AI/Data Engineering role in New York, NY or Bangalore. You will design and build data pipelines that capture high-volume system activity and convert it into structured data for downstream use, while sessionizing logs and applying LLMs for structured outputs.

You’ll own data quality, privacy, and work closely with the founding team to ship features end-to-end in a fast-paced startup setting.

Qualifications

• 1-4 years of experience in data engineering, AI/ML engineering, or backend data-focused work.
• Strong Python and/or TypeScript, SQL proficiency, and ability to build trusted data pipelines.
• Hands-on experience with LLMs, structured output, prompting, and non-determinism handling.
• Appreciation for measurable data quality and privacy in handling sensitive information.

Responsibilities

• Design and build data pipelines that convert high-volume system activity into structured data.
• Turn raw activity streams into processes by sessionizing logs, clustering sequences, and using LLMs with structured output.
• Develop the evaluation backbone with synthetic data generation to measure discovery pipeline quality.
• Own data quality and privacy, collaborating with founders and engineering to ship features end to end.

Skills

Python

TypeScript

SQL

Data pipelines

LLMs

Education

Degree in computer science

Tools

PostgreSQL

pgvector

Cloud infrastructure

Job description

TalentProof is seeking a founding Applied-AI/Data Engineering role in New York, NY or Bangalore. You will design and build data pipelines that capture high-volume system activity and convert it into structured data for downstream use, while sessionizing logs and applying LLMs for structured outputs.

You’ll own data quality, privacy, and work closely with the founding team to ship features end-to-end in a fast-paced startup setting.