AI Team Lead

About the role

Requirements

• We are seeking an experienced AI Team Lead to lead onsite AI delivery in Saudi Arabia. The role involves managing AI projects, supervising technical teams, designing AI architectures, developing enterprise-grade solutions, and ensuring successful deployment of AI systems into production.
• The candidate should have strong experience in Generative AI, machine learning, deep learning, data science, AI solution architecture, MLOps/LLMOps, cloud deployment, and project management.
• 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, including 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, TensorFlow, 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, TensorFlow, Scikit-learn, Hugging Face, LangChain, or LlamaIndex.
• - 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 planning, estimation, risk management, stakeholder communication, and delivery tracking.
• - Ability to communicate complex technical concepts clearly to business and technical stakeholders.
• - Excellent problem-solving, analytical, leadership, and decision-making skills.
• - Excellent command of English, both written and spoken.
• - Willingness to work onsite in Saudi Arabia.
• Nice to Have
• - Experience in government, healthcare, finance, telecom, or regulated enterprise environments.
• - Experience with GPU-based model training and inference optimization.
• - Experience with computer vision, OCR, document AI, speech AI, or multimodal AI systems.
• - Experience with Azure OpenAI, Oracle AI Services, AWS Bedrock, Google Vertex AI, or similar enterprise AI platforms.
• - Knowledge of AI governance, responsible AI, data privacy, cybersecurity, and compliance frameworks.
• - Experience in presales, technical proposal preparation, and client-facing solution presentations.
• - Arabic langua

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, including 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, TensorFlow, 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.

Skills

• Generative AI
• machine learning
• deep learning
• data science
• MLOps
• LLMOps
• LLMs
• RAG architectures
• NLP
• computer vision
• PyTorch
• TensorFlow
• Scikit-learn
• AI agents
• chatbots
• microservices
• APIs
• cloud deployment