AI Lead

Job descriptionYou will lead Valify’s Machine Learning department end to end, overseeing the models powering our identity-verification products and the engineers who build them. The role covers Computer Vision and NLP, with a strong focus on production deployment, evaluation, version control, and rollback procedures.This is a hands-on leadership role in a regulated environment, with models deployed on-premises, through APIs, and mobile SDKs. You will report to the Technology Lead, manage and mentor the ML team, and represent the function in technical and regulatory discussions.A key responsibility is building and growing the team through hiring, development, and performance management, while ensuring the department meets its KPIs and continuously improves its engineering and operating standards.Responsibilities

• Own model quality across our entire stack: benchmarks, regression gates, and release sign-off beforeanything reaches a client environment.
• Lead and grow the ML team — supervision, technical review, development plans, and hiring.
• Set the technical direction for the department’s highest-risk services and oversee the response toemerging fraud techniques.
• Lead the research and proof-of-concept development behind new products and capabilities.
• Direct the annotation team: scope labeling requirements, set annotation guidelines, and sign off ondata quality before it enters training.
• Maintain the evidence trail that regulated clients and auditors require.
• Build evaluation and telemetry infrastructure across all deployments.Requirements
• 5+ years of professional experience in machine learning or applied AI, including at least 2 yearsleading engineers or owning a production ML pipeline.
• Bachelor’s degree in computer science, computer engineering, or a related quantitative field; apostgraduate degree is a plus.
• Deep expertise in computer vision — detection, OCR, classification, segmentation, and image qualityassessment, or comparable computer vision domains — with working competence in NLP.
• Proven ownership of ML systems in production, covering deployment, monitoring, maintenance, androllback.
• Demonstrated experience optimizing model performance — latency and resource footprint —including quantization, pruning, or distillation for inference on constrained client hardware.
• Proven experience leading a model evaluation process.
• Experience overseeing the data annotation process end to end, from scoping through quality control.
• Experience managing engineers directly, including technical review and development planning.
• Proven experience in deployment in constrained environments: on-premises or air-gappedenvironments, client-controlled infrastructure, or regulated industries.Valued but not required
• Prior work on identity verification, biometrics, KYC, or document understanding.
• Experience with Arabic OCR, including optimizing, training, or fine-tuning such models.
• Exposure to regulator-facing work: audits, model documentation, compliance evidence.