AI Engineer (Saudi Only)

Lucidya; Saudi Arabia; On-site; SAR 120,000 - 150,000; Full time

Job summary

A leading AI solutions company in Saudi Arabia is looking for an AI Engineer to design and develop AI-driven solutions that tackle complex business issues. You will oversee the entire AI lifecycle, ensuring production-ready models and scalable architectures. Candidates should have strong Python skills and experience with machine learning frameworks like TensorFlow or PyTorch. Join us to mentor junior engineers and elevate the organization's AI capabilities in a fast-paced environment.

Qualifications

• 2-3 years of hands-on experience in AI, machine learning, or related engineering roles.
• Strong proficiency in Python; Java or other languages is a plus.
• Deep experience with machine learning and deep learning frameworks such as TensorFlow or PyTorch.

Responsibilities

• Lead the design, development, and optimization of machine learning and deep learning models.
• Own the end-to-end AI lifecycle from problem definition to model deployment.
• Architect scalable data pipelines for training on large datasets.

Skills

Machine learning

Deep learning

Python

Problem-solving

Cross-functional collaboration

Tools

TensorFlow

PyTorch

AWS

GCP

Azure

Job description

As an AI Engineer, you will lead the design, development, and production deployment of AI‑driven solutions that solve complex business problems at scale.

This role goes beyond model training—you will take ownership of AI system architecture, influence technical direction, and ensure models are production‑ready, scalable, and aligned with business objectives.

You will play a key role in advancing the organization’s AI capabilities and mentoring other engineers while driving innovation across multiple AI domains.

Key Responsibilities

• Lead the design, development, and optimization of machine learning and deep learning models for real‑world, production use cases.
• Own the end‑to‑end AI lifecycle, from problem definition and data exploration to model deployment, monitoring, and iteration.
• Architect and implement scalable, efficient data pipelines for training and inference on large and complex datasets.
• Evaluate model performance using robust metrics, conduct error analysis, and continuously improve model accuracy and reliability.
• Build and deploy neural networks using TensorFlow, PyTorch, or similar frameworks, ensuring production‑grade quality.
• Collaborate closely with data engineers, software engineers, product managers, and domain experts to translate business needs into AI solutions.
• Drive technical decision‑making around model selection, system architecture, and trade‑offs (performance, cost, scalability).
• Ensure AI solutions follow ethical AI principles, data privacy standards, and regulatory requirements.
• Contribute to and review technical documentation, design proposals, and best practices for AI development.
• Mentor junior engineers and contribute to raising the overall technical bar of the AI team.
• Stay current with industry trends and research, and assess the adoption of new techniques or tools where they add real value.
• Support and improve model deployment, monitoring, and observability in production environments.

Required Qualifications

• 2‑3 years of hands‑on experience in AI, machine learning, or related engineering roles.
• Strong proficiency in Python (Java or other languages is a plus).
• Deep experience with machine learning and deep learning frameworks such as TensorFlow or PyTorch.
• Proven experience working with large‑scale datasets and building production‑grade AI systems.
• Solid understanding of model evaluation, optimization, and performance trade‑offs.
• Experience deploying AI models into production, with attention to scalability, reliability, and efficiency.
• Strong problem‑solving skills and the ability to translate complex business challenges into AI‑driven solutions.
• Excellent communication skills and experience working in cross‑functional teams.
• Nice to have: Experience with MLOps, model monitoring, and CI/CD for AI systems.
• Exposure to cloud platforms (AWS, GCP, Azure) for AI workloads.
• Experience in domains such as NLP, computer vision, recommendation systems, or predictive analytics.
• Prior experience mentoring or leading other engineers.