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AI Engineer
PlayStation
Abu Dhabi, UAE
Part Time
Mid
2 days ago
PythonPyTorchTensorFlowScikit learnLarge Language Models (LLMs)Generative AI
Free
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About the Role
We are seeking an AI Engineer to design, develop, deploy, and optimize intelligent solutions using AI and ML technologies. You will work on the full AI development lifecycle, from data preparation to deployment, and collaborate with cross-functional teams.
Key Skills for This Role
PythonPyTorchTensorFlowScikit learnLarge Language Models (LLMs)Generative AI
Responsibilities
- Design, develop, deploy, and optimize intelligent solutions that leverage artificial intelligence and machine learning technologies
- Build scalable AI systems, integrate machine learning models into production environments, and collaborate with cross functional teams
- Work on the full AI development lifecycle, from data preparation and model development to deployment, monitoring, optimization, and continuous improvement
- Develop machine learning models, integrate large language models (LLMs) and generative AI technologies into applications
- Build intelligent automation solutions, design Retrieval Augmented Generation (RAG) pipelines, implement AI agents, and optimize AI workflows
- Evaluate foundation models, improve prompt strategies, manage inference pipelines, and enhance AI system performance through experimentation
- Design APIs and AI services, integrate cloud based AI platforms, implement vector search and knowledge retrieval systems
- Monitor model performance, maintain secure, maintainable, and scalable AI infrastructure
- Contribute to AI architecture, model evaluation, testing frameworks, observability, governance, and responsible AI practices
- Stay current with emerging AI technologies, open source frameworks, research advancements, and industry best practices
Requirements
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Information Technology, Mathematics, or a related discipline
- Strong understanding of artificial intelligence, machine learning, deep learning, and statistical learning principles
- Proficiency in Python and familiarity with software engineering best practices, object oriented programming, and API development
- Knowledge of machine learning frameworks such as PyTorch, TensorFlow, JAX, Scikit learn, or equivalent
- Familiarity with large language models (LLMs), generative AI, transformer architectures, foundation models, and prompt engineering techniques
- Understanding of Retrieval Augmented Generation (RAG), embeddings, semantic search, vector databases, and knowledge retrieval systems
- Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, Haystack, or equivalent
- Knowledge of AI agent architectures, tool calling, workflow orchestration, conversational memory, and intelligent automation concepts
- Experience integrating AI models with RESTful APIs, cloud services, enterprise systems, and modern software applications
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform and their AI services
- Understanding of Docker, Kubernetes, CI/CD pipelines, Git version control, and modern DevOps practices
- Knowledge of SQL, NoSQL databases, data pipelines, and distributed systems
Full Job Posting
Role Description
- We are seeking an AI Engineer to design, develop, deploy, and optimize intelligent solutions that leverage artificial intelligence and machine learning technologies to solve complex business challenges.
- This role is responsible for building scalable AI systems, integrating machine learning models into production environments, and collaborating with cross functional teams to deliver innovative, reliable, and high performance AI powered applications.
- The successful candidate will work on the full AI development lifecycle, from data preparation and model development to deployment, monitoring, optimization, and continuous improvement.
Key Responsibilities
- Develop machine learning models, integrate large language models (LLMs) and generative AI technologies into applications.
- Build intelligent automation solutions, design Retrieval Augmented Generation (RAG) pipelines, implement AI agents, and optimize AI workflows for performance, scalability, and reliability.
- Evaluate foundation models, improve prompt strategies, manage inference pipelines, and enhance AI system performance through experimentation and continuous optimization.
- Design APIs and AI services, integrate cloud based AI platforms, implement vector search and knowledge retrieval systems, monitor model performance, and maintain secure, maintainable, and scalable AI infrastructure.
- Contribute to AI architecture, model evaluation, testing frameworks, observability, governance, and responsible AI practices while ensuring compliance with organizational standards.
- Stay current with emerging AI technologies, open source frameworks, research advancements, and industry best practices to continuously improve AI capabilities and accelerate innovation.
Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Information Technology, Mathematics, or a related discipline.
- Strong understanding of artificial intelligence, machine learning, deep learning, and statistical learning principles.
- Proficiency in Python and familiarity with software engineering best practices, object oriented programming, and API development.
- Knowledge of machine learning frameworks such as PyTorch, TensorFlow, JAX, Scikit learn, or equivalent technologies.
- Familiarity with large language models (LLMs), generative AI, transformer architectures, foundation models, and prompt engineering techniques.
- Understanding of Retrieval Augmented Generation (RAG), embeddings, semantic search, vector databases, and knowledge retrieval systems.
- Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, LangGraph, Semantic Kernel, Haystack, or equivalent technologies.
- Knowledge of AI agent architectures, tool calling, workflow orchestration, conversational memory, and intelligent automation concepts.
- Experience integrating AI models with RESTful APIs, cloud services, enterprise systems, and modern software applications.
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform and their AI services.
- Understanding of Docker, Kubernetes, CI/CD pipelines, Git version control, and modern DevOps practices.
- Knowledge of SQL, NoSQL databases, data pipelines, and distributed systems.
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