The Training & Quality Manager AI/LLM Practice will lead global training and quality initiatives for AI/LLM projects, ensuring operational excellence, consistency, and compliance. The role focuses on building scalable training frameworks, standardizing quality processes, and driving continuous improvement across global teams. You'll collaborate with cross-functional and client stakeholders to strengthen delivery performance and enhance overall project quality in alignment with AI/LLM goals.
Key Responsibilities
- Lead global training and quality programs for AI/LLM projects ensuring consistency, accuracy, and compliance.
- Design and implement scalable training frameworks, assessments, and certification programs.
- Monitor performance metrics, identify skill gaps, and drive targeted upskilling initiatives.
- Establish and enforce quality standards and continuous improvement processes across teams.
- Conduct audits, calibrations, and root cause analyses to maintain excellence in delivery.
- Partner with operations and client teams to align training and quality strategies with business goals.
- Drive change management, governance, and cross-functional collaboration across global sites.
Requirements:
- Bachelor's degree required (Computer Science, IT, Engineering, Linguistics, or related field preferred).
- 5+ years of experience in training leadership and quality management within AI, LLM, tech operations, or a process-driven environment.
- Strong understanding of AI/LLM concepts, model training processes, and data-driven decision-making.
- Proven track record of leading cross-functional, multicultural teams in a fast-paced global setup.
- Excellent English communication, presentation, and stakeholder management skills.
- Analytical mindset with the ability to design and monitor performance dashboards and quality metrics.
- Demonstrated problem-solving, innovation, and change management capabilities.
Good to Have:
- Knowledge of process improvement methodologies (Six Sigma, Lean, Kaizen).
- Experience working with AI model evaluation, policy compliance, or linguistic quality frameworks.
- Exposure to client-facing roles in AI or technology projects.
- Strong people leadership and mentoring skills.
