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Generative AI Engineer
Civil Engineer & Researcher (CER)
Abu Dhabi, UAE
Parttime
Mid
3 days ago
PythonLarge Language Models (LLMs)Retrieval Augmented Generation (RAG)LangChainLlamaIndexLangGraph
Free
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PythonLarge Language Models (LLMs)Retrieval Augmented Generation (RAG)
About the Role
We are seeking a Generative AI Engineer to design, develop, deploy, and optimize intelligent applications powered by modern generative AI technologies. The role focuses on building scalable AI solutions using LLMs, multimodal models, RAG, AI agents, and foundation models.
Key Skills for This Role
PythonLarge Language Models (LLMs)Retrieval Augmented Generation (RAG)LangChainLlamaIndexLangGraph
Responsibilities
- Design, develop, deploy, and optimize intelligent applications powered by modern generative AI technologies
- Build scalable AI solutions using large language models (LLMs), multimodal models, retrieval augmented generation (RAG), AI agents, and foundation models
- Design end to end generative AI workflows, integrating AI models into software applications
- Develop AI applications using foundation models, implementing prompt engineering strategies, building retrieval pipelines, integrating vector databases, and optimizing inference performance
- Design agentic workflows, implement tool calling capabilities, manage conversational memory, and improve AI responses through prompt optimization, context management, and model evaluation
- Integrate external APIs, enterprise knowledge bases, databases, and cloud services into AI applications
- Evaluate different AI models, benchmark performance, monitor production systems, and continuously improve model quality, latency, reliability, and cost efficiency
- Contribute to AI architecture design, model deployment strategies, testing methodologies, observability, security, and governance practices
- Collaborate with cross functional teams to transform business requirements into intelligent AI powered products
Requirements
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Information Technology, or a related discipline
- Strong understanding of generative AI, large language models (LLMs), foundation models, and transformer architectures
- Proficiency in Python and familiarity with software engineering best practices, object oriented programming, and API development
- Knowledge of AI frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, Semantic Kernel, or similar orchestration platforms
- Familiarity with OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, or other commercial and open source foundation models
- Understanding of prompt engineering, prompt optimization, structured outputs, function calling, and AI agent architectures
- Knowledge of Retrieval Augmented Generation (RAG), embeddings, vector databases, semantic search, and knowledge retrieval systems
- Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, or equivalent technologies
- Understanding of AI evaluation methodologies, benchmarking, hallucination mitigation, grounding, and response quality assessment
- Experience integrating AI services with REST APIs, databases, cloud platforms, and enterprise applications
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform and their AI services
- Knowledge of Docker, containerization, CI/CD pipelines, Git version control, and modern software deployment practices
Full Job Posting
Role Description
- We are seeking a Generative AI Engineer to design, develop, deploy, and optimize intelligent applications powered by modern generative artificial intelligence technologies.
- This role focuses on building scalable AI solutions using large language models (LLMs), multimodal models, retrieval augmented generation (RAG), AI agents, and foundation models to solve complex business challenges and enhance user experiences.
- The successful candidate will be responsible for designing end to end generative AI workflows, integrating AI models into software applications, and developing reliable, secure, and production ready AI systems.
Key Responsibilities
- Develop AI applications using foundation models, implementing prompt engineering strategies, building retrieval pipelines, integrating vector databases, optimizing inference performance, and creating reusable AI components.
- Design agentic workflows, implement tool calling capabilities, manage conversational memory, and improve AI responses through prompt optimization, context management, and model evaluation.
- Integrate external APIs, enterprise knowledge bases, databases, and cloud services into AI applications to enable intelligent automation and contextual reasoning.
- Evaluate different AI models, benchmark performance, monitor production systems, and continuously improve model quality, latency, reliability, and cost efficiency.
- Contribute to AI architecture design, model deployment strategies, testing methodologies, observability, security, and governance practices that support scalable enterprise AI adoption.
- Collaborate with cross functional teams to deliver innovative AI solutions aligned with organizational objectives.
Qualifications
- Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Information Technology, or a related discipline.
- Strong understanding of generative AI, large language models (LLMs), foundation models, and transformer architectures.
- Proficiency in Python and familiarity with software engineering best practices, object oriented programming, and API development.
- Knowledge of AI frameworks such as LangChain, LlamaIndex, LangGraph, Haystack, Semantic Kernel, or similar orchestration platforms.
- Familiarity with OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, or other commercial and open source foundation models.
- Understanding of prompt engineering, prompt optimization, structured outputs, function calling, and AI agent architectures.
- Knowledge of Retrieval Augmented Generation (RAG), embeddings, vector databases, semantic search, and knowledge retrieval systems.
- Familiarity with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, or equivalent technologies.
- Understanding of AI evaluation methodologies, benchmarking, hallucination mitigation, grounding, and response quality assessment.
- Experience integrating AI services with REST APIs, databases, cloud platforms, and enterprise applications.
- Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform and their AI services.
- Knowledge of Docker, containerization, CI/CD pipelines, Git version control, and modern software deployment practices.
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