AI Data Engineer for Private Credit

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 directly 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.