Technical Training & Quality Manager
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Job Description
Technical Training & Quality Manager leading AI data training, quality assurance, and continuous improvement. Supporting data annotation, LLM evaluation, and Generative AI programs at Innodata.
Responsibilities:
- 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 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, identify root causes, and implement corrective and preventive actions
- Drive continuous improvement to improve accuracy, consistency, productivity, and turnaround time
- Provide technical guidance for data annotation, LLM evaluation, RLHF, prompt-response evaluation, NLP, multimedia annotation, Generative AI evaluation, data validation, model benchmarking, and red teaming projects
- Translate project guidelines, client specifications, annotation taxonomies, and evaluation rubrics into training and quality programs
- Work with SMEs and technical teams to resolve complex quality and interpretation issues
- Collaborate 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
- Measure training ROI and quality improvement using data and analytics
- Drive automation and technology adoption in training and quality processes
- Standardize best practices across projects and delivery teams
Requirements:
- 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 / 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
- 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
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