AI Engineer

Lucidya | لوسيديا; Riyadh; On-site; SAR 80,000 - 120,000; Full time

Job summary

A leading AI solutions company seeks an experienced AI Engineer to drive the design and deployment of AI-driven solutions. You will lead the end-to-end AI lifecycle, from model development to deployment, ensuring solutions are scalable and aligned with business objectives. The ideal candidate has 2-3 years experience in AI/ML, strong proficiency in Python, and deep expertise with TensorFlow or PyTorch. This role offers an opportunity to mentor junior engineers while advancing the organization's AI capabilities.

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.
• Experience deploying AI models into production, emphasizing scalability, reliability, and efficiency.
• Solid understanding of model evaluation and performance trade-offs.

Responsibilities

• Lead the design and optimization of ML and DL models.
• Own the end-to-end AI lifecycle from data exploration to model deployment.
• Architect efficient data pipelines for large datasets.
• Evaluate model performance and conduct error analysis.
• Collaborate with cross-functional teams to translate business needs into AI solutions.
• Mentor junior engineers and raise the technical bar of the AI team.

Skills

Python

Machine learning frameworks

Deep learning frameworks

Problem-solving

Communication skills

Tools

TensorFlow

PyTorch

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.

Requirements

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.