AI Data Quality & Engineering Lead

3across; United States; Remote; USD 100,000 - 140,000; Full time

Job summary

A technology solutions firm is seeking an experienced AI Data Quality & Engineering Lead to manage a dynamic team and ensure the highest quality standards in data annotation. This remote position requires strong expertise in data quality management and the ability to optimize workflows. Candidates should have at least 3 years of experience in relevant roles and proficiency in SQL and Python. Interested applicants can send their resumes to the provided email.

Qualifications

• 3+ years in data quality management, data operations, or related roles in AI/ML or data annotation environments.
• Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects.
• Experience in managing and developing remote or distributed teams.

Responsibilities

• Develop and document robust quality assurance processes and standard operating procedures (SOPs).
• Conduct in-depth data analysis to identify quality issues.
• Lead, mentor, and train a team of Data Quality Analysts.

Skills

SQL

Data Management

Python

AI

R

Excel

Data Quality

Model Validation

Data Operations

LABELSTUDIO

Model Management

F SCORE

Education

Bachelors degree in a technical field

Tools

Labelbox

Dataloop

LabelStudio

Job description

Overview

Job Title: AI Data Quality & Engineering Lead

Experience: 6+ Years

Location: Remote

Headquarters: Gurugram, Chennai, Noida, Indore, Mumbai

Primary Skills: SQL, Data Management, Python, AI, R, Excel, Data Quality, Model Validation, Data Operations, LABELSTUDIO, Model Management, F SCORE

Job Description

We are looking for an experienced AI Data Quality & Engineering Lead to oversee a dynamic, high-performing team that ensures the highest quality standards across data annotation and quality assurance (QA) processes.

The ideal candidate will be a technical expert with a strong focus on data quality management, annotation processes, and continuous improvement.

You will be responsible for optimizing workflows, driving automation innovation, and collaborating with cross-functional teams to ensure data integrity and consistency at scale.

Key Responsibilities

• Strategic Leadership
  • Develop and document robust quality assurance processes and standard operating procedures (SOPs) to ensure high-quality annotation outputs.
  • Define and implement comprehensive quality metrics (e.g., F1 score, inter-annotator agreement) that align with business objectives and industry standards.
  • Proactively identify and mitigate risks within data annotation workflows, driving continuous improvements to reduce errors and increase efficiency.
  • Serve as the subject matter expert (SME) for data annotation quality, providing feedback, training, and support to annotators and project teams to uphold the highest standards of data accuracy.
• Analysis & Reporting
  • Conduct in-depth data analysis to identify quality issues, assess the effectiveness of quality strategies, and uncover root causes of recurring errors.
  • Create and maintain dashboards that provide real-time insights into quality metrics, trends, and project performance.
  • Prepare detailed quality reports for senior leadership and clients, clearly articulating quality trends, risks, and actionable improvement plans.
  • Collaborate with cross-functional teams (e.g., operations, engineering, and client services) to align project goals and ensure consistent quality assurance initiatives.
• Operational Leadership
  • Lead, mentor, and train a team of Data Quality Analysts, fostering a culture of accountability, precision, and continuous improvement.
  • Manage the configuration and integration of annotation and QA tools (e.g., Labelbox, Dataloop, LabelStudio) to ensure alignment with project requirements and optimal tool performance.
  • Evaluate, implement, and drive the adoption of innovative quality control tools and automation technologies to streamline workflows, enhance operational efficiency, and improve overall quality control processes.

Education

• Bachelors degree in a technical field (e.g., Computer Science, Data Science) or equivalent professional experience.
• Experience: 3+ years in data quality management, data operations, or related roles in AI/ML or data annotation environments.
• Proven track record in designing and executing quality assurance strategies for large-scale, multi-modal data annotation projects.
• Experience in managing and developing remote or distributed teams.

Technical Expertise

• In-depth knowledge of data annotation processes, quality assurance methodologies, and statistical quality metrics (e.g., F1 score, inter-annotator agreement).
• Strong data-analysis skills with proficiency in tools like Excel and Google Sheets for reporting, and familiarity with programmatic analysis techniques (e.g., Python, SQL).
• Proficiency with annotation and QA tools such as Labelbox, Dataloop, and LabelStudio.
• Familiarity with core AI/ML pipeline concepts, including data preparation, model training, and model evaluation.

Preferred Qualifications

• Experience in fast-paced tech environments with exposure to AI/ML pipelines and agile methodologies.
• Background in managed services or vendor-driven environments.
• Experience with prompt engineering or large-language-model-assisted workflows to optimize annotation and validation processes.
• Strong knowledge of ethical AI practices and compliance frameworks, ensuring that AI models are fair, transparent, and unbiased.

If interested, please share your resume to [e-mail]