Expert Team Lead, SWE
New
J
JobgetherAI data operations
Listing location: South Africa; Structured job location: South AfricaFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 1+ years of hands-on experience in data labeling, annotation, AI training or evaluation, content quality, or a comparable operational discipline.
- Required Skills
- SQLPeople ManagementSoftware EngineeringCoaching
Requirements
- Have 1+ years of hands-on experience in data labeling, annotation, AI training or evaluation, content quality, or a comparable operational discipline.
- Bring professional experience building or contributing to scalable, high-performance technology applications, particularly in software engineering or another technical environment.
- Understand data quality metrics, annotation workflows, guideline-driven processes, and quality assurance practices.
- Have basic knowledge of artificial intelligence and machine learning concepts, including how training and evaluation data influence model performance.
- Have experience with people management, coaching, training-program development, performance management, or certification frameworks.
- Be able to manage multiple projects, prioritize effectively, and work in a fast-moving environment.
- Bring strong analytical capabilities and familiarity with KPI-driven operational improvement; SQL experience is a plus.
- Have expertise in at least one relevant technical or specialist domain, such as finance, STEM, or coding/software engineering.
- Combine strategic thinking with hands-on execution and remain directly involved in technical and operational work.
- Be based in an eligible country supported by the organization.
Responsibilities
- Own end-to-end quality and delivery for assigned human data projects, personally reviewing technical work for accuracy, consistency, guideline adherence, and reliable data production.
- Lead, coach, and performance-manage AI Tutors, including conducting reviews, maintaining development records, creating improvement plans, and supporting professional development.
- Participate in labeling, annotation, evaluation, and review activities.
- Establish and enforce quality assurance processes, taxonomy standards, workflow guidelines, and evaluation criteria.
- Monitor quality scores, throughput, and send-back rates to identify bottlenecks and improve team efficiency.
- Develop training materials, practice exercises, and certification benchmarks, and manage certification processes and workforce adjustments.
- Partner with data managers, team leads, and engineering stakeholders to translate AI model requirements into labeling strategies and operational processes.
- Document project outcomes, identify process improvements, and communicate project status, risks, and results to stakeholders.
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