Technical Training & Quality Manager – AI Data

Innodata (Nasdaq: INOD), a global data engineering company, specializes in enabling responsible AI advancement through data, evaluation frameworks, and expertise. They provide solutions for Generative AI and AI builders with a 36+ year legacy of delivering high-quality data and outcomes.

Scope of the Role:

We are seeking a Technical Training & Quality Manager – AI Data to lead technical training, capability development, quality assurance, and continuous improvement for AI data programs.

The role involves close collaboration with AI/ML, Data Science, Operations, Program Management, and Quality teams to ensure teams working on data annotation, data preparation, model evaluation, RLHF, LLM training, AI response evaluation, and other AI-data workflows have the required technical capabilities and consistently meet client-defined quality standards.

The ideal candidate combines a strong technical understanding of AI/ML and data workflows with hands-on experience in training, quality management, process improvement, and large-scale operations.

What You’ll Own:

Technical Training & Capability Development:

• Design and execute technical training programs for AI data and annotation teams.
• Develop training curricula, learning paths, assessments, certification programs, and refresher modules.
• Train teams on AI/ML concepts, LLMs, Generative AI, NLP, data annotation, data labeling, model evaluation, prompt engineering, RLHF, and AI response quality.
• Conduct Train-the-Trainer programs and build internal technical trainers.
• Identify skill gaps through assessments, production performance, and quality metrics, creating targeted upskilling plans.
• Develop practical exercises, technical assessments, simulations, and certification frameworks.

Quality Management:

• Own quality frameworks and standards across AI data projects.
• Define and monitor quality KPIs, including accuracy, agreement rates, defect rates, audit scores, rework, and productivity.
• Establish quality calibration processes and conduct regular quality audits.
• Analyze quality trends and identify root causes of recurring defects.
• Partner with Operations and Program Managers to implement corrective and preventive actions.
• Drive continuous improvement initiatives to improve accuracy, consistency, productivity, and turnaround time.

AI Data & Technical Operations:

• Provide technical guidance for projects involving:
• Data annotation and labeling
• LLM evaluation
• RLHF / human feedback
• Prompt-response evaluation
• NLP and text classification
• Image/video/audio annotation
• Generative AI evaluation
• Data validation and enrichment
• Model benchmarking and red teaming
• Understand project guidelines, client specifications, annotation taxonomies, and evaluation rubrics, translating them into effective training and quality programs.
• Work with Subject Matter Experts (SMEs) and technical teams to resolve complex quality and interpretation issues.

Stakeholder Management:

• Work closely with clients, Program Managers, Operations, Engineering, Data Science, and QA teams.
• Participate in client calibration sessions and quality reviews.
• Present quality dashboards, training effectiveness, RCA findings, and improvement plans to senior leadership.
• Support new project launches through training needs analysis, SOP development, quality framework creation, and readiness assessments.

Continuous Improvement:

• Identify opportunities to improve training effectiveness, operational quality, and process efficiency.
• Use data and analytics to measure training ROI and quality improvement.
• Drive automation and technology adoption in training and quality processes.
• Standardize best practices across projects and delivery teams.

You’ll Thrive in This Role If You Have:

• 8–12 years of experience in AI/ML, data operations, data annotation, AI training, quality management, technical L&D, or related areas.
• Bachelor’s/Master’s degree in Computer Science, Engineering, Data Science, AI/ML, Statistics, or a related field.
• Strong understanding of Artificial Intelligence, Machine Learning, Generative AI, and LLMs.
• Experience working with AI data, annotation, and model evaluation projects.
• Experience managing training and quality teams in a high-volume delivery environment.
• Strong analytical and problem-solving skills.
• Experience with Root Cause Analysis, CAPA, calibration, quality audits, and process improvement.
• Strong stakeholder and client management skills.
• Excellent communication, presentation, and facilitation skills.
• Ability to convert complex technical concepts into easy-to-understand training content.
• Certifications in AI/ML, Quality Management, Six Sigma, instructional design, or technical training are preferred.
• Experience with AI platforms, annotation tools, LLM evaluation frameworks, or data-quality platforms.
• Exposure to Python, SQL, analytics/BI tools, or automation would be an advantage.