Technical Training & Quality Manager

Innodata Inc.

1d•$145,000 - $175,000

About The Position

Innodata is a global data engineering company focused on enabling the responsible advancement of artificial intelligence. We provide data, evaluation frameworks, and human expertise for building trustworthy AI systems at scale. Our mission is to support Generative AI/AI builders and adopters with a range of transferable solutions, platforms, and services, building on our 36+ year legacy of delivering high-quality data and outstanding customer outcomes. This role is responsible for leading technical training, capability development, quality assurance, and continuous improvement for AI data programs. The Technical Training & Quality Manager will collaborate with AI/ML, Data Science, Operations, Program Management, and Quality teams to ensure that teams involved in data annotation, preparation, model evaluation, RLHF, LLM training, AI response evaluation, and other AI-data workflows possess the necessary technical skills and consistently adhere to client-defined quality standards. The ideal candidate will possess a strong technical understanding of AI/ML and data workflows, coupled with practical experience in training, quality management, process improvement, and large-scale operations.

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.

Nice To Haves

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.

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 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.
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 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.