Data Validator – AI/ML Systems

Prismforce; Mumbai; On-site; INR 600,000 - 900,000; Full time

Job summary

Prismforce in Mumbai, India is seeking a Data Validator to join the Data Science Quality / AI Quality team to ensure data accuracy powering AI features.

You will review AI-generated data, validate relationships, and log errors, collaborating with data science and product teams to refine validation workflows and maintain high-quality standards across our platform.

Ideal candidates have 1–5 years in data QA or ML evaluation, strong attention to detail, and experience with Excel or Airtable.

Qualifications

• 1–5 years of experience in data QA, content validation, annotation, or ML evaluation.
• Strong attention to detail and ability to identify inaccuracies.
• Clear written communication for documentation and feedback.
• Comfortable with structured and unstructured data.
• Experience collaborating with data science, product, or engineering teams.
• Exposure to LLMs or evaluation of generative AI outputs.
• Experience with content curation or tagging projects.

Responsibilities

• Review AI-generated content for accuracy, consistency, completeness, and clarity.
• Validate relationships, classifications, and recommendations from automated systems.
• Compare outputs from different models and provide structured qualitative feedback.
• Identify and log errors, inconsistencies, or hallucinations in data or AI responses.
• Collaborate with data science and product teams to refine validation workflows.
• Use internal tools or dashboards to track validations, feedback, and patterns over time.
• Contribute to SOPs, guidelines, and quality benchmarks.

Skills

Data QA

Content validation

Annotation tools

ML evaluation

LLMs evaluation

Attention to detail

Written communication

Structured data

Cross-functional collaboration

QA dashboards

Tools

Excel

Google Sheets

Airtable

QA dashboards

Job description

Job Title: Data Validator – AI/ML Systems

Location: [Location / Remote]

Type: Full-time

Experience Level: 1–5 years

Team: Data Science Quality / AI Quality

About the Role

We are looking for detail-oriented and analytically strong professionals to join our Data Validation (DV) team , responsible for ensuring the accuracy and reliability of data that powers a wide range of AI/ML-based features in our platform.

As a Data Validator, you will be a core part of the human-in-the-loop process that reviews and evaluates AI-generated data, model outputs, and system-generated relationships to maintain high-quality standards across our products.

What You’ll Do

Review AI-generated content for accuracy, consistency, completeness, and clarity

Validate relationships, classifications, and recommendations produced by automated systems

Compare outputs from different models and provide structured qualitative feedback

Identify and log errors, inconsistencies, or hallucinations in data or AI responses

Collaborate with data science and product teams to refine validation workflows

Use internal tools or dashboards to track validations, feedback, and patterns over time

Contribute to the development of SOPs, guidelines, and quality benchmarks

What We’re Looking For

1–5 years of experience in data QA, content validation, annotation, or ML evaluation

Strong attention to detail and ability to identify semantic or factual inaccuracies

Clear written communication for documentation and feedback reporting

Comfortable working with structured and unstructured data

Familiarity with working alongside data science, product, or engineering teams

Exposure to LLMs (e.g., ChatGPT, Claude) or evaluation of generative AI outputs

Prior experience with content curation, annotation tools, or ontology/tagging projects

Comfort with basic tools like Excel, Google Sheets, Airtable, or QA dashboards

Why Join Us

Work on cutting-edge AI systems with direct business impact

Be part of a fast-growing team that blends human insight with ML automation

Gain exposure to real-world applications of data quality and AI evaluation

Opportunities for growth into QA leadership, prompt validation, or ML feedback ops