Founding Applied AI Engineer

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

Verify skills before you apply#VERIFIEDLocationNew York, BangaloreThis is a founding applied-AI role. You'll be building our core data pipeline and intelligence layer with the founding team from 0-1. You'll be tackling our largest technical challenges across technologies.We are hiring in New York and Bangalore.### What you'll do* Design and build data pipelines that capture and turn high volumes of system activity into structured process data.* Turn raw activity streams into processes: sessionize event logs, cluster recurring sequences, and use LLMs with structured output to name, score, and conformance-check them.* Build the evaluation backbone from scratch: stand up synthetic data generation pipelines that produce ground-truth datasets, and use them to measure and improve the quality of our discovery pipeline and model outputs.* Own data quality and privacy.* Partner closely with the founders and the rest of engineering to ship features end to end.### What we're looking for* 1-4 years of experience in data engineering, AI/ML engineering, or backend work with a data focus (strong internships and projects count).* Strong in Python and/or TypeScript, comfortable in SQL, and able to build data pipelines you can trust - moving, transforming, and validating data.* Hands-on experience working with LLMs - structured output, prompting, and wrangling non-determinism - whether at work or in serious side projects.* A practical sense for evaluation: you know that "it looks right" isn't the same as "it's measurably right," and you're excited to build the data and harnesses that prove it.* Care about data privacy and handling sensitive information responsibly.* Comfort with ambiguity and a real appetite to own a hard, open-ended problem.**Nice to have*** Background in process mining, sequence / event-log analysis, or workflow analytics - a genuine differentiator for this role.* Deeper PostgreSQL: window functions, partitioning, pg\_cron, query performance.* Embeddings and vector search (pgvector) or semantic retrieval.* Familiarity with cloud infrastructure.* Any prior early-stage startup experience.* Degree in computer science or a related field.### Benefits & Perks* Equity* Medical* Flexible time off* Mac