Key Responsibilities:

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 and create 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, 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, LLM evaluation, RLHF, prompt-response evaluation, NLP, image/video/audio annotation, Generative AI evaluation, data validation, and model benchmarking.

- 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.

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.

Required Skills & Experience:

- 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, or 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.

Preferred Qualifications:

- 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.

- Tier 1 or tier 2 institutes preferred.