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AI Team Lead

Job in Riyadh, Riyadh Region, Saudi Arabia
Listing for: InnovationTeam
Full Time position
Listed on 2026-07-10
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 360000 - 520000 SAR Yearly SAR 360000.00 520000.00 YEAR
Job Description & How to Apply Below
Position: AI Team Lead at InnovationTeam

Apply for AI Team Lead at Innovation Team in الرياض, S01, SA. This full‑time on site position offers great opportunities for career growth. Innovation Team is a forward‑thinking technology company specialized in delivering advanced AI‑driven solutions for enterprises and government organizations. We are currently seeking a highly skilled AI Team Lead to join our team onsite in Saudi Arabia.

As an AI Team Lead at Innovation Team, you will be responsible for leading the design, development, implementation, and delivery of end‑to‑end AI solutions across multiple domains, including Generative AI, machine learning, deep learning, data science, computer vision, NLP, intelligent automation, and enterprise AI systems.

Key Responsibilities
  • Lead the AI team in designing, developing, and delivering enterprise AI solutions.
  • Manage AI projects from requirement gathering to production deployment.
  • Translate business needs into technical AI solution designs and implementation plans.
  • Design and supervise solutions involving Generative AI, LLMs, RAG systems, AI agents, NLP, machine learning, deep learning, and computer vision.
  • Lead data science initiatives, including data analysis, feature engineering, model development, evaluation, and optimization.
  • Build and oversee end‑to‑end AI pipelines; data ingestion, preprocessing, model training, inference, monitoring, and continuous improvement.
  • Guide the implementation of machine learning and deep learning models using frameworks such as PyTorch, Tensor Flow, Scikit‑learn, and related tools.
  • Supervise the development of AI agents, chatbots, recommendation systems, classification models, predictive analytics, and intelligent automation solutions.
  • Define AI architecture, integration patterns, APIs, microservices, and deployment strategies.
  • Ensure AI solutions are scalable, secure, reliable, cost‑efficient, and production‑ready.
  • Collaborate with business stakeholders, product teams, data teams, software engineers, and cloud teams.
  • Manage task allocation, technical reviews, project milestones, risks, and delivery timelines.
  • Establish best practices for MLOps, LLMOps, model governance, model monitoring, documentation, and quality assurance.
  • Support presales and client discussions by providing technical input, solution proposals, effort estimation, and feasibility assessments.
  • Ensure AI solutions comply with security, privacy, governance, and responsible AI principles.
  • Mentor and coach AI engineers, ML engineers, data scientists, and junior team members.
  • Stay up‑to‑date with the latest developments in AI, machine learning, deep learning, Generative AI, cloud AI services, and enterprise AI platforms.
Required

Skills & Qualifications
  • Minimum 5 years of professional experience in AI, machine learning, data science, or related technology fields.
  • Minimum 2–3 years of experience leading AI teams or managing AI solution delivery.
  • Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or a related field.
  • Strong hands‑on experience in machine learning, deep learning, NLP, Generative AI, and data science.
  • Solid understanding of LLMs, RAG architectures, AI agents, vector databases, prompt engineering, and model evaluation.
  • Strong experience with Python and AI/ML frameworks such as PyTorch, Tensor Flow, Scikit‑learn, Hugging Face, Lang Chain, or Llama Index.
  • Experience in building and deploying ML/DL models for real‑world enterprise use cases.
  • Strong understanding of supervised learning, unsupervised learning, classification, regression, clustering, recommendation systems, forecasting, and anomaly detection.
  • Experience with data preparation, data quality assessment, feature engineering, model training, validation, testing, and performance monitoring.
  • Strong software engineering knowledge, including REST APIs, microservices, Docker, Kubernetes, CI/CD, and Git.
  • Experience deploying AI workloads on cloud platforms such as OCI, Azure, AWS, or GCP; OCI experience is preferred.
  • Knowledge of MLOps and LLMOps practices, including model versioning, monitoring, retraining, evaluation, and deployment automation.
  • Strong project management skills, including…
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