322 AI Programmer jobs in Saudi Arabia

Key Responsibilities:

• Design and implement AI and ML solutions using Azure AI services, including Azure OpenAI, Azure AI Foundry, and Azure Machine Learning.
• Fine-tune and deploy LLMs (e.g., GPT-4o, GPT-3.5) for enterprise use cases using Azure infrastructure.
• Build AI-powered applications using Python frameworks such as FastAPI and Streamlit, Gradio.
• Develop and manage RAG pipelines and integrate them with enterprise data sources.
• Utilize Azure AI Foundry for model lifecycle management, experimentation, and deployment.
• Leverage Azure Functions and Logic Apps to orchestrate AI workflows and automate business processes.
• Integrate AI capabilities into Power Platform (Power Apps, Power Automate) for low-code/no-code solutions.
• Build conversational AI experiences using Microsoft Bot Framework and integrate with Microsoft Teams.
• Enable intelligent document processing and collaboration by integrating AI with SharePoint.
• Conduct prompt engineering and use orchestration tools like Semantic Kernel and function calling.
• Ensure scalability, security, and performance of AI solutions deployed on Azure.
• Collaborate with cross-functional teams to integrate AI capabilities into existing systems.
• Stay updated with the latest advancements in AI, NLP, and Azure technologies.

About Cognizant:

Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at or @cognizant.

Job Type:
Internship

Contract length:
4 weeks

Internship Type:
Unpaid

About Knodemy

Knodemy is a U.S.-based AI education company transforming how students learn technology. Our platform integrates AI-powered teaching tools with localized curriculum to deliver future-ready skills to youth globally. We work across the U.S., GCC, and South Asia with a mission to close the digital skills gap.

Website:

Why This Role Matters

This internship is ideal for students passionate about technology, education, and community empowerment. You'll gain hands-on experience in AI product development and also play a critical role in teaching and mentoring K–12 learners in Saudi Arabia.

Key Responsibilities

Assist in the development and testing of Knodemy's AI-driven education platform (50% of role)

• Work with frontend/backend tools (React, , Firebase, Python, etc.)
• Integrate AI models and APIs (e.g., OpenAI, HuggingFace)
• Participate in UI/UX refinement and QA testing

Deliver tech training and mentorship to students in Riyadh (50% of role)

• Conduct sessions on AI, app design, and digital safety
• Support local instructors and help localize global curriculum
• Collect user feedback and iterate on instructional materials

Ideal Candidate

Currently pursuing a Bachelor's degree (4th year) or recently graduated in:

• Computer Science
• AI / Machine Learning
• Software Engineering

• Educational Technology or related fields

• Strong programming foundation in JavaScript, HTML/CSS, and ideally React or Vue

• Familiarity with backend (Python, , Firebase) and AI/ML APIs (a plus, not required)
• Clear communicator in English (Arabic fluency is required)
• Passion for teaching, mentorship, and empowering youth with tech skills
• Availability for 10–15 hours/week, including flexibility to join U.S. timezone calls

Requirements

• Personal laptop with working mic and camera
• Stable internet connection for live collaboration
• Interest in working with school-age learners (Grades K–12)

What You'll Gain

• Project experience in AI development and education delivery
• Mentorship from U.S.-based engineers, educators, and entrepreneurs
• Letter of recommendation and internship certificate
• Access to Knodemy's global alumni and instructor network
• Priority consideration for a full-time role with Knodemy USA

Key Responsibilities:

• Design and implement AI and ML solutions using Azure AI services, including Azure OpenAI, Azure AI Foundry, and Azure Machine Learning.
• Fine-tune and deploy LLMs (e.g., GPT-4o, GPT-3.5) for enterprise use cases using Azure infrastructure.
• Build AI-powered applications using Python frameworks such as FastAPI and Streamlit, Gradio.
• Develop and manage RAG pipelines and integrate them with enterprise data sources.
• Utilize Azure AI Foundry for model lifecycle management, experimentation, and deployment.
• Leverage Azure Functions and Logic Apps to orchestrate AI workflows and automate business processes.
• Integrate AI capabilities into Power Platform (Power Apps, Power Automate) for low-code/no-code solutions.
• Build conversational AI experiences using Microsoft Bot Framework and integrate with Microsoft Teams.
• Enable intelligent document processing and collaboration by integrating AI with SharePoint.
• Conduct prompt engineering and use orchestration tools like Semantic Kernel and function calling.
• Ensure scalability, security, and performance of AI solutions deployed on Azure.
• Collaborate with cross-functional teams to integrate AI capabilities into existing systems.
• Stay updated with the latest advancements in AI, NLP, and Azure technologies.

About Cognizant:
Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at or @cognizant.

Key Responsibilities

• Design and implement AI and ML solutions using Azure AI services, including Azure OpenAI, Azure AI Foundry, and Azure Machine Learning.
• Fine-tune and deploy LLMs (e.g., GPT-4o, GPT-3.5) for enterprise use cases using Azure infrastructure.
• Build AI-powered applications using Python frameworks such as FastAPI and Streamlit, Gradio.
• Develop and manage RAG pipelines and integrate them with enterprise data sources.
• Utilize Azure AI Foundry for model lifecycle management, experimentation, and deployment.
• Leverage Azure Functions and Logic Apps to orchestrate AI workflows and automate business processes.
• Integrate AI capabilities into Power Platform (Power Apps, Power Automate) for low-code/no-code solutions.
• Build conversational AI experiences using Microsoft Bot Framework and integrate with Microsoft Teams.
• Enable intelligent document processing and collaboration by integrating AI with SharePoint.
• Conduct prompt engineering and use orchestration tools like Semantic Kernel and function calling.
• Ensure scalability, security, and performance of AI solutions deployed on Azure.
• Collaborate with cross-functional teams to integrate AI capabilities into existing systems.
• Stay updated with the latest advancements in AI, NLP, and Azure technologies.

About Cognizant
Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at or @cognizant.

At

, we are building cutting-edge AI systems that combine
large language models, computer vision, and voice AI
to solve real-world challenges. We are looking for a
Senior AI Developer
who can design, build, and scale production-grade AI pipelines — from research to deployment — across cloud and edge environments.

Key Responsibilities

• Build & deploy end-to-end AI systems (LLMs, computer vision, voice transcription, multimodal AI) using Python, PyTorch/TensorFlow, and Docker/Kubernetes.
• Optimize AI models: fine-tune/train LLMs (e.g., GPT-4, Llama 2, RAG pipelines), vision models (CNNs, ViTs), and speech models (Whisper, VITS) for low-latency inference.
• Engineer AI pipelines: design scalable data preprocessing, training, and serving pipelines (Ray, Kubeflow, Airflow).
• Manage edge/cloud deployment: containerize models (Docker) and deploy on Kubernetes, AWS SageMaker, or edge devices.
• Performance tuning: benchmark and optimize models for GPU/TPU acceleration (CUDA, TensorRT).

Required Skills

5+ years in Python AI development (production experience required, not just research).

Hands-on with LLMs (LangChain, Hugging Face), computer vision (OpenCV, YOLO), and voice AI (ASR, TTS).

Strong MLOps skills: Docker, CI/CD for AI, model registries (MLflow, Weights & Biases).

Experience with distributed training frameworks (FSDP, DeepSpeed, Horovod).

Nice-to-Have

• NVIDIA Triton Inference Server, ONNX Runtime.
• Model optimization: quantization, pruning.
• CUDA-level performance debugging.

If you're passionate about scaling AI systems into production and want to work on impactful projects, we'd love to connect.

Roles & Responsibilities:

• Design and implement machine learning models, algorithms, and deep learning applications and systems.
• Optimize and scale ML models for production.
• Collaborate with data scientists, administrators, data analysts, data engineers, and data architects on production systems and applications.
• Monitor model performance and identify differences in data distribution that could potentially affect model performance in real-world applications.
• Ensure algorithms generate accurate user recommendations.
• Prepare and clean data for model training, including data wrangling, feature engineering, and handling missing values.
• Integrate machine learning models into production systems (web applications, APIs) using software engineering best practices.
• Document the machine learning development process and model performance for future reference and collaboration.
• Stay up to date with developments in the machine learning industry.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related field (preferred).
• At least 5 years of hands-on experience as machine learning engineer or similar role.
• Familiarity with Python, Java, C++, and R.

• Machine Learning Algorithms and Techniques (supervised, unsupervised, reinforcement learning).
• Software Engineering Principles (version control, testing, DevOps).
• Cloud Computing Platforms (AWS, Azure, GCP) (often a plus).
• Extensive math and computer skills, with a deep understanding of probability, statistics, and algorithms.
• In-depth knowledge of machine learning frameworks, like Keras or PyTorch.
• Familiarity with data structures, data modeling, and software architecture.
• Excellent time management and organizational skills.

Role Overview

We are looking for a driven and experienced Machine Learning Engineer with strong

specialization in Computer Vision to join our AI team. This role involves developing high

performance computer vision solutions that power real-world applications across diverse

domains such as surveillance, retail analytics, document automation, inspections, and beyond.

You will contribute across the full lifecycle of model development. from problem definition to

deployment. working closely with multidisciplinary teams to transform business requirements

into scalable intelligent systems.

Role Summary

As a Machine Learning Engineer with a focus on Computer Vision, you will bridge research and

real-world deployment. Your mission is to transform complex visual data into actionable

intelligence, whether it's through real-time video analytics, document understanding, or

automated inspection. You will work cross-functionally to build, optimize, and scale machine

learning models that are robust, efficient, and impactful in operational environments.

Key Responsibilities

• Design and develop machine learning models tailored for image, video, or document

analysis tasks such as classification, detection, segmentation, or text extraction.

• Build and maintain robust end-to-end pipelines for data ingestion, preprocessing, model

training, validation, and deployment.

• Experiment with new algorithms and architectures to improve model performance and

adaptability across environments.

• Collaborate with hardware, software, and product teams to integrate vision models into

cloud-based and on-edge environments.

• Analyze performance metrics and continuously optimize models for accuracy, latency,

and resource efficiency.

• Support team members with code reviews, architecture planning, and research

explorations.

• Ensure models and pipelines follow best practices in versioning, testing, and

documentation.

Minimum Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field with a

strong focus on machine learning or computer vision.

• 4–7 years of professional experience in designing and deploying ML solutions, with at

least 3 years focused on computer vision.

• Solid understanding of deep learning principles and core CV concepts such as image

transformations, feature extraction, and object recognition.

• Strong coding and debugging skills in a major programming language used in AI/ML

development.

• Experience in handling large-scale image/video datasets and building automated training

workflows.

• Ability to evaluate model performance using appropriate statistical and business metrics.

Preferred Qualifications

• Experience deploying models into production environments, both cloud-based and

edge-oriented.

• Familiarity with video processing, multi-modal data fusion, or document layout analysis.

• Background in solving real-world problems involving noisy data, low-resource

environments, or high accuracy demands.

• Exposure to annotation workflows, human-in-the-loop training, or active learning

setups.

• Strong communication skills, with ability to document findings, present solutions, and

collaborate across disciplines.

Role Description

This is a full-time, on-site role for a Machine Learning Engineer based in Dammam. The Machine Learning Engineer will be responsible for developing and implementing machine learning models, analyzing large datasets, and collaborating with cross-functional teams to enhance our gamified psychometric assessments. Day-to-day tasks include pattern recognition, data preprocessing, optimizing algorithms, and testing neural network models to enhance predictive accuracy and performance.

Qualifications

• Strong knowledge in Pattern Recognition and Statistics
• Proficiency in Computer Science concepts and Algorithms
• Experience with Neural Networks
• Excellent problem-solving and analytical skills
• Ability to work collaboratively in a team environment
• Bachelor's/Master's degree in Computer Science, Machine Learning, or a related field

We're looking for a Machine Learning Engineer who is deeply grounded in ML theory and excited to design, train, fine-tune, and deploy Large Language Models (LLMs) and other ML systems in real-world production environments.

You'll work closely with backend and product individuals/teams to deliver smart, scalable features—from rapid experimentation to full-scale deployment. If you're passionate about ML theory, hands-on with LLMs, and know how to ship high-impact AI features, this role is for you.What You'll Do

• Design and implement ML solutions from ideation to production
• Fine-tune and integrate LLMs
• Deploy and monitor LLM-powered features at scale in real-world products
• Collaborate with engineers and product teams to build intelligent, user-facing features
• Write clean, scalable code and detailed technical documentation
• Stay current with the latest in ML research, LLM capabilities, and MLOps best practices

Must-Haves

1. Be an Arabic speaker
2. Have at least 1 year of non-internship experience in Machine Learning.
3. Strong ML and DL theory background, you don't just use things, you know how they are working under the hood.
4. Experience training and fine-tuning LLMs, with practical knowledge of transformer architectures
5. Solid production-level Python experience and strong software engineering fundamentals (OOP, OOD, DSA)
6. Familiarity with LLM integration frameworks like HuggingFace Transformers, OpenAI, or LangChain
7. Familiarity with ML data pipelines and manipulation tools (e.g., Pandas, NumPy)
8. Strong research, writing, and documentation skills
9. Collaborative mindset and ability to communicate technical ideas clearly

Nice-to-Haves

• Experience deploying LLM-based features to production
• Knowledge of parameter-efficient fine-tuning (LoRA, QLoRA, PEFT)
• Familiarity with RAG pipelines and vector databases (e.g., Pinecone, Weaviate)
• Understanding of model serving and inference optimization (quantization, batching)
• Exposure to MLOps practices (monitoring, versioning, CI/CD for ML)
• Experience with RESTful APIs, Docker, and cloud platforms (GCP, AWS, or Azure)
• Interest in NLP applications, smart assistants, or chatbot systems