Technical Training & Quality Manager

Innodata(Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked.

Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted  provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

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

The role will work closely with AI/ML, Data Science, Operations, Program Management, and Quality teams to ensure that 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 should combine strong technical understanding of AI/ML and data workflows with hands-on experience in training, quality management, process improvement, and large-scale operations

• 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 and create targeted upskilling plans.
• Develop practical exercises, technical assessments, simulations, and certification frameworks.

• Own quality frameworks and standards across AI data projects.
• Define and monitor quality KPIs, 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.

• Data annotation and labeling
• LLM evaluation
• NLP and text classification
• Generative AI evaluation
• Data validation and enrichment
• Model benchmarking and red teaming
• Understand project guidelines, client specifications, annotation taxonomies, and evaluation rubrics and translate them into effective training and quality programs.
• Work with SMEs and technical teams to resolve complex quality and interpretation issues.

• 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.
• 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.