About Daloopa, Inc.

Daloopa accelerates decision-making for investment professionals by transforming complex financial data into actionable intelligence through AI-powered infrastructure. Founded by Thomas Li, Daloopa automates the extraction, organization, and delivery of deeply sourced, audit-ready financial datasets, enabling clients to update models faster, reduce errors, and focus on higher-value analysis. With innovations like MCP for Financial Services, which connects verified data directly to AI agents and industry workflows, Daloopa is trusted by leading global institutions such as Morgan Stanley and supported by prominent investors.

The Role

Daloopa's mission is to become the market leader in high-quality, actionable data for the world's top investment professionals. We're judged on three things: coverage, speed, and accuracy. Whether a hedge fund analyst can find the right number, get it fast, and trust it absolutely.

The companies in our space (AlphaSense, , Refinitiv, Fiscal.ai and surface." Daloopa is solving ground truth at scale. FinancialReports.eu) mostly solve "find What makes us different is the loop. Our analysts review, correct, and enrich every extraction from financial documents.

Those corrections become training signal for the next generation of our models. Models get sharper. Analysts get faster. Coverage expands, speed compounds, and accuracy keeps climbing in a way LLM-only competitors can't match.

That's why Morgan Stanley trusts our numbers, and why this company has a moat.

As a Senior Backend Engineer on the ML side, you'll build the platform that makes the loop work.



The systems that turn analyst corrections into structured training signal. The pipelines that move PDFs and HTML filings into model-ready data, route extraction work to humans when models are uncertain, and feed the result back into the next training cycle.

The domain spans every public market, every accounting convention, every reporting nuance across geographies, and the platform you build has to scale across all of it. If you're excited by AI systems where humans and models actually collaborate (rather than chatbots pretending to know things), this is the role.

What You'll Lead and Transform:

- Build and scale the supervised learning platform that turns analyst corrections into training data.
- The connective tissue between our human-in-the-loop process and our models.
- Design and operate the extraction pipelines that turn PDFs and HTML filings into structured, audit-ready data, in partnership with the ML team.
- Build the routing and uncertainty-aware systems that decide when a model can act alone and when an analyst needs to weigh in.
- Integrate LLMs and ML models into production via API endpoints and inference pipelines, with the reliability and observability our clients require.
- Define database schemas and architectural decisions for performance, scalability,



and resilience across a domain that spans every public market and accounting convention.
- Champion clean, maintainable code through reviews, tests, and transparent documentation.

What Sets You Up for Success

- 4+ years of professional backend engineering experience.
- Strong expertise in Python and Django (or equivalent backend frameworks).
- Hands-on experience integrating ML models or LLMs into production: model serving, inference APIs, vector stores.
- Deep understanding of relational and non-relational databases (PostgreSQL, MySQL, Redis, DynamoDB).
- Experience with distributed systems, caching, and asynchronous task queues (Celery or equivalent).
- Track record of leading backend or ML-integrated projects end-to-end.
- Genuine interest in the problem domain. Financial data, supervised learning systems, or human-in-the-loop AI.
- You should be the kind of person who reads the AlphaSense product launches and has opinions.

Bonus Points for

- Experience building human-in-the-loop or active learning systems where labeled data quality directly determines model performance.
- Experience extracting structured data from messy real-world documents (PDFs, HTML, scanned content) at scale.
- Background in fintech, financial data, or other domains where data quality is mission-critical and audit trails matter.
- Familiarity with the financial fundamentals landscape: 10-Ks, 10-Qs, transcripts, segment reporting, non-GAAP reconciliations.
- Prior experience at growth-stage startups where you've scaled systems through major growth phases.

📌 Senior Backend Engineer - ML/LLM (Noida)
🏢 Daloopa
📍 Noida