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.
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