Firmable is the market-leading B2B sales intelligence platform in Asia Pacific — and we're scaling that success globally at pace. Backed by leading investors and 2,000+ customers robust, we exist to give sales teams an unfair advantage: the deepest company and people data of any platform, enriched with real-time signals, served at the right moment by intelligent agents.
Our data is the product. Building it now means building with LLMs in the pipeline, and knowing exactly when to trust them.
The Role
As Data Engineer — Data Platform, you'll build the pipelines that turn billions of raw records into the clean, modelled datasets our product, analytics, and AI systems depend on. Some of that is classic data engineering: SQL, dbt, orchestration, warehouse performance. A growing share is not: LLM-based extraction, enrichment, and validation running inside the pipeline at scale.
The hard part isn't calling a model. It's making a probabilistic component behave like a reliable one: labelled eval sets, precision and recall you can measure, versioned prompts, cost ceilings,



and the harnesses that catch regressions when a vendor silently changes the model under you.
This is a hands-on engineering role with real ownership. You'll own reliability, cost, and whether downstream consumers can trust the data, whether it came from a SQL join or a language model.
What You'll Own
• LLM-in-the-pipeline systems — extraction, enrichment, entity resolution, and semantic validation steps that run over millions of records a day with structured outputs, retries, and human-review fallbacks

• Eval harnesses — labelled sets, scorers, and regression suites that gate every prompt or model change; precision/recall tracked per check, not vibes

• Rules vs. LLMs — deterministic checks (dbt tests, data contracts, SQL) wherever structure allows; LLMs where semantic judgement is needed; the discipline to know which is which

• Cost and drift — token budgets per pipeline, model routing (cheap models for

📌 Data Engineer (India)
🏢 Firmable
📍 India