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Home/Jobs/LLM engineer
Master-Works logo
Master-Works

LLM engineer

🇸🇦 Riyadh, Saudi Arabia🏢 On-site
LLMGenerative AIPythonVector DatabasesRAGAI/MLNLP
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About the Role:

Master works is looking for an experienced LLM Engineer to join our team working on next-generation AI-driven analytics solutions. The ideal candidate will design and implement natural language search, semantic search solutions, and generative AI features for our web applications. You will work hands-on with advanced language models, vector databases, RAG pipelines, and build intelligent workflows to enable actionable insights from both structured and unstructured data.

Key Responsibilities:

• Develop and maintain natural language search capabilities and semantic search solutions using vector databases.
• Implement NL-to-SQL pipelines and other intelligent query generation features for analytics platforms.
• Build and optimize Generative AI features (summarization, recommendations, insights) for analytics applications.
• Design and implement Agents and Workflows for complex tasks across datasets.
• Develop RAG (Retrieval-Augmented Generation) pipelines for knowledge retrieval and query resolution.
• Create embeddings for structured and unstructured data and leverage them for semantic understanding and search.
• Process and transform structured (tables, CSV, SQL) and unstructured data (text, documents, logs) as input/output for AI models.
• Collaborate with data engineers, analysts, and product teams to integrate AI features seamlessly into the platform.
• Evaluate, benchmark, and optimize LLM performance for real-world analytics use cases. Required Qualifications & Skills:

• Bachelor's or master's degree in computer science, AI/ML, Data Science, or related field.
• Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, or similar).
• Experience with vector databases such as Pinecone, Weaviate, Milvus, or FAISS.
• Strong understanding of embeddings, semantic search, and their practical applications.
• Expertise in RAG pipelines, Agents, and Workflow orchestration.
• Proficiency in Python and relevant AI/ML frameworks (LangChain, HuggingFace, PyTorch, TensorFlow).
• Familiarity with NL-to-SQL systems and natural language query processing.
• Experience working with structured and unstructured data sources.
• Knowledge of data pipelines, preprocessing, and storage formats for analytics.
• Ability to design scalable, production-ready AI solutions integrated with web applications. Preferred Qualifications:

• Experience working with AI based chatbots, Natural language search, NL to SQL.
• Knowledge of prompt engineering and fine-tuning LLMs.
• Familiarity with data privacy, security, and compliance considerations in AI pipelines. Soft Skills:

• Strong problem-solving and analytical skills.
• Excellent communication skills to work with cross-functional teams.
• Passion for AI and staying up to date with the latest developments in LLMs and generative AI.

Requirements

  • •Bachelor's or master's degree in computer science, AI/ML, Data Science, or related field
  • •Hands-on experience with LLMs (OpenAI, Anthropic, Cohere, or similar)
  • •Experience with vector databases (Pinecone, Weaviate, Milvus, or FAISS)
  • •Strong understanding of embeddings, semantic search
  • •Expertise in RAG pipelines, Agents, and Workflow orchestration
  • •Proficiency in Python and AI/ML frameworks (LangChain, HuggingFace, PyTorch, TensorFlow)
  • •Familiarity with NL-to-SQL systems
  • •Experience working with structured and unstructured data

Nice to Have

  • •Experience working with AI based chatbots
  • •Knowledge of prompt engineering and fine-tuning LLMs
  • •Familiarity with data privacy, security, and compliance considerations

Responsibilities

  • •Develop and maintain natural language search capabilities and semantic search solutions
  • •Implement NL-to-SQL pipelines
  • •Build and optimize Generative AI features
  • •Design and implement Agents and Workflows
  • •Develop RAG pipelines
  • •Create embeddings for structured and unstructured data
  • •Process and transform structured and unstructured data
  • •Collaborate with data engineers, analysts, and product teams

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