AI Data Engineer for Private Credit

Job description We are representing a high-performing boutique private credit investment firm building proprietary AI capability internally.

They are not hiring a data support analyst.

They are hiring the engineer who will help build the AI backbone of an investment platform.

This role is for the top 5% of early-career engineers who want ownership, commercial exposure, and the chance to build systems that dire ctly influence capital allocation decisions.

The Mandate

Design and build the data infrastructure that will power:

• AI-assisted underwriting

• Portfolio risk surveillance

• Automated covenant monitoring

• LLM-driven document intelligence

• Proprietary credit analytics

You will work directly with investors deploying capital — not in a siloed tech team.

Your work will influence live investment decisions.

What Makes This Different

• No legacy bureaucracy

• No passive dashboard maintenance

• Direct access to decision-makers

• High accountability

• Visible impact

This is a build environment.

The firm is early in its AI journey.

The right candidate will shape architecture, tooling, and standards.

What You’ll Actually Do

• Build scalable ETL/ELT pipelines from loan systems and financial data

• Structure complex borrower reporting (financial statements, PDFs, credit memos)

• Design clean datasets for predictive credit risk models

• Enable LLM/RAG pipelines for document intelligence

• Implement data quality, validation, and monitoring frameworks

• Partner with credit investors to translate underwriting logic into data systems

This is production engineering in a high-stakes financial environment.

Who We’re Looking For

You are likely:

• 1–3 years into your engineering career

• Strong in Python and SQL

• Comfortable working in cloud environments (AWS/GCP/Azure)

• Experienced building real pipelines — not just notebooks

• Curious about how financial systems actually work

Bonus points for:

• Exposure to ML workflows

• Familiarity with dbt, Airflow, Docker

• Experience handling financial or semi-structured data

• Interest in LLM infrastructure and vector databases

Finance background is not required.

Intellectual horsepower and ownership mentality are.

This Role Is Not For You If

• You prefer clearly defined, low-risk task lists

• You want heavy supervision

• You are uncomfortable working directly with senior stakeholders

• You are looking for a purely academic ML role

Upside

• Direct learning from investors

• Rapid technical growth

• Path toward AI Engineer / ML Engineer / Quant Data roles

• High visibility within a compact, performance-driven firm

• Compensation aligned to performance

This is an opportunity to build proprietary AI systems inside a capital allocation business — early.

For the right engineer, this is career-accelerating.

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