About the role
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 growing 2,000+ customers strong, 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. We're not building another search box. We're building the engine that tells a salesperson exactly who to call, why, and what to say — before they even ask.
The Role This isn't a traditional QA role where you write test plans and chase tickets. As an Applied AI Engineer at Firmable, you'll own data quality across our platform end-to-end — designing the systems that decide whether the data flowing in is trustworthy enough to ship. You'll spend your time deep in Snowflake, Python, and LLMs — architecting LLM-based quality checks, building the eval harnesses that hold them accountable, designing the skills library that other agents and teammates depend on, and shipping agentic remediation pipelines where rules, LLMs, and humans each do what they're best at. This role has deep technical ownership . You'll make the hard rule-vs-LLM calls, build the production systems behind them, and translate findings into recommendations that change what we build.
What You'll Own Data Analysis & Product Intelligence Investigate complex datasets using SQL, Snowflake, Python, and LLMs — directing agents to do the heavy lifting while you frame the question and judge the answer Unlock insights in large-scale B2B datasets that shape product direction and commercial strategy Build scalable, reusable datasets — and the skills that make them queryable by any agent or teammate Translate findings into recommendations that move metrics, not slides that summarise what happened Data Quality & Source Management Own data quality end-to-end across rule-based and LLM-based checks, side by side — design the checks, run the evals, monitor drift, fix root causes Make the rule-vs-LLM judgement call on every check: deterministic logic where rules win, LLMs where semantic, contextual, or entity-resolution nuance is needed — and justify the split Assess and onboard new data sources: coverage, freshness, accuracy, and where LLM judges add lift over deterministic profiling Track down the hardest data bugs and fix them at the root, partnering with engineering and product Scrape or source supplementary data when it sharpens insights or enriches the product AI-Powered Analysis & Automation Architect LLM-based quality checks with explicit rubrics, structured outputs, and labelled eval sets — precision/recall measured, not vibed Build and maintain the skills library ( SKILL.md specs) that powers recurring workflows — quality checks, remediation proposals, dataset onboarding — versioned, documented, and invocable by any agent or teammate Ship agentic remediation pipelines : triage with rules, escalate to LLMs, propose fixes, log every call (prompt version, model, cost, latency, decision), surface human-review queues Own the eval and observability scaffolding — prompt versioning, traces, drift detection when a vendor silently updates claude-sonnet-latest, cost ceilings with token math behind them Set the model-choice playbook — Haiku for cheap classification, Sonnet for nuanced judgement, frontier models for hard edge cases — and revise it as model economics shift Cross-Functional Support Be the technical point of escalation for data quality across product, engineering, and go-to-market — the person trusted when a number is questioned Partner with product and engineering on architecture decisions where data quality is in the loop Build the monitoring surface stakeholders operate against: DQ trend by check type (rule vs. LLM), top failing reasons surfaced by LLM judges, cost and latency over time, human-review backlog
