Role & responsibilities

The AI/LLM Dataset Engineer (Senior) is the technical lead for the golden dataset and QA design workstreams on the Cleris Pulse NLQ Evaluation Framework. This role owns the seeded synthetic data generation pipeline, statistical distribution engineering, Jinja2 QA template library, and all schema documentation. The engineer coordinates two Domain SMEs and the API Integration Engineer on a day-to-day basis, reporting to the Engagement Lead.

Preferred candidate profile

Synthetic Data Pipeline
Design and implement the seeded data generation pipeline: numpy.random.default_rng(seed=42) + Faker 24.x + pandas 2.x
Engineer realistic statistical distributions: Pareto =1.16 for revenue, Poisson =3.2 for event frequency, Gaussian mixture for date clustering
Implement controlled imperfection injection: ~1% duplicate rows, 23% NULLs (Bernoulli p=0.025), 0.5% outlier records, boundary date values
Run FK integrity gate: DuckDB PRAGMA foreignkeycheck must pass before any CSV file is exported




Produce SHA-256 manifest.json per dataset and manage Git LFS versioning (tag: dataset-v1.0.0)
QA Design & Verification
Build the Jinja2 SQL template library for all 5 question tiers (T1–T5) per domain
Verify all expected_answers by executing reference SQL against the golden dataset in DuckDB before inclusion
Collaborate with Domain SMEs on natural-language question phrasing and business context accuracy
Author judge_reference answers for each QA pair to support LLM-as-Judge calibration

Schema & Documentation
Produce ER diagrams for all 5 domains using dbdiagram.io; present for TechM/Cleris approval
Draft DDL SQL per domain; run SQLFluff linting before presenting for stakeholder review
Produce the CSV Header Specification document for joint sign-off with Cleris
Produce Data Dictionaries (field name, type, description, sample values, constraints) for all domain tables

📌 Ai Llm Dataset Engineer Senior Telangana (India)
🏢 Codehive Labs Hyderabad
📍 India