Data Operations Lead

Insight Global

United States

On-site

USD 82,656 - 137,760

Full time

14 days+
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Benefits offered by this job

Medical insurance
Vision insurance
401(k)

Job summary

A leading technology staffing firm is seeking a Data Operations Lead to oversee large-scale LLM training programs. This contract role requires a seasoned professional with over 10 years of experience in technical delivery and managing cross-functional teams. The successful candidate will drive the generation of high-quality datasets, manage stakeholder expectations, and ensure technical feasibility across projects. Benefits include medical insurance, vision insurance, and 401(k).

Qualifications

  • 10+ years of experience building and leading large-scale technical delivery teams.
  • Experience managing large-scale dataset generation for LLMs.
  • Technical fluency in machine learning concepts and modern ML tooling.

Responsibilities

  • Lead the generation of high-quality scalable LLM training datasets.
  • Manage the end-to-end data lifecycle from customer intake to delivery.
  • Serve as the primary contact for large engagements.

Skills

Large-scale technical delivery
Cross-functional team management
Data generation for LLMs
Quality review mechanisms
Communication skills

Education

Bachelor’s degree in Engineering or Computer Science

Tools

HuggingFace
LangChain
Weights & Biases

Job description

Base pay range

$60.00/hr - $100.00/hr

Position: Data Operations Lead

Duration: 12 month contract + possible extensions

Pay Rate: $60-$100/hr.

About the Role

We are seeking a seasoned, techno-functional leader to drive the development and execution of large‑scale LLM training programs. This role combines deep technical expertise with strong operational leadership and customer‑facing engagement. You will oversee the creation of high‑quality datasets, annotation workflows, and scalable data pipelines that power foundational model training.

Key Responsibilities
  • Lead the generation and delivery of high‑quality, scalable LLM training datasets focused on SFT, RLHF, rubric‑based evaluation, reasoning, and agentic workflows.
  • Manage the end‑to‑end data lifecycle—from customer intake to delivery—including requirements gathering, annotation workflow design, quality metrics, and delivery setup.
  • Serve as the primary point of contact for large engagements; manage stakeholder expectations and delivery timelines.
  • Collaborate with engineering, product, and research teams to ensure technical feasibility and alignment across workstreams.
  • Define and document best practices for prompt evaluation, data schema design, and human‑in‑the‑loop QA processes.
  • Mentor and manage leads, program managers, and annotators across multiple AI training pods.
  • Act as a strategic partner to customers, providing insights on trade‑offs, resourcing, and performance metrics.
  • Drive continuous improvement in data quality, tools, and processes by harvesting reusable assets and refining workflows.
Required Qualifications
  • 10+ years of experience building and leading large‑scale technical delivery teams, including managing cross‑functional teams of 100+ members.
  • Bachelor’s degree in Engineering, Computer Science, or equivalent practical experience leading technical initiatives.
  • Proven experience managing large‑scale dataset generation or annotation for LLMs, ideally with RLHF or SFT pipelines.
  • Strong understanding of quality review mechanisms (e.g., prompt win rate, agreement metrics, inter‑annotator consistency, preference modeling).
  • Experience with human data generation across at least one modality (Text, Image, Video, Audio).
  • Technical fluency in data platforms, machine learning concepts, and modern ML tooling (e.g., HuggingFace, LangChain, Weights & Biases).
  • Excellent communication skills with experience presenting to executive stakeholders and managing client escalations.
Seniority level

Mid‑Senior level

Employment type

Contract

Job function

Design

Benefits
  • Medical insurance
  • Vision insurance
  • 401(k)

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