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Senior Machine Learning Engineer (NLP/LLMs)
Hudhud Maps
Riyadh, KSA
Full-time
Senior · 5+ years experience
Onsite
Discovered 3 weeks ago
Natural Language Processing (NLP)Large Language Models (LLMs)LangChainHaystackPythonPyTorch
Free
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Natural Language Processing (NLP)Large Language Models (LLMs)LangChain
Role Overview
We are looking for a Senior Machine Learning Engineer with deep expertise in Natural Language Processing (NLP) and Large Language Models (LLMs) to join our growing team.
This role requires strong research-to-production skills, combined with robust software engineering capabilities to ensure scalable, reliable systems.
Key Skills for This Role
Natural Language Processing (NLP)Large Language Models (LLMs)LangChainHaystackPythonPyTorch
Full Job Posting
Key Responsibilities
- Design, develop, and deploy NLP and LLM-based solutions for real-world applications.
- Translate research papers and experimental models into production-ready systems .
- Architect and implement scalable ML pipelines, ensuring performance and reliability.
- Collaborate with product and engineering teams to integrate ML models into Hudhud’s platforms.
- Conduct experiments, evaluate models, and optimize for accuracy, efficiency, and cost.
- Stay up to date with the latest advancements in NLP, LLMs, and generative AI.
- Guide junior engineers and contribute to building a high-performance AI team.
Qualifications & Skills
- Education: MSc or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related fields.
- Experience: Minimum 5+ years of professional experience in ML/NLP, with at least 3+ years working on LLMs.
- Core Expertise: Strong background in NLP techniques (transformers, embeddings, sequence models). Hands-on experience with LLMs (fine-tuning, prompt engineering, model evaluation). Practical experience with LangChain and/or Haystack frameworks for building LLM-powered applications. Solid understanding of ML system design and deployment in production environments.
- Strong background in NLP techniques (transformers, embeddings, sequence models).
- Hands-on experience with LLMs (fine-tuning, prompt engineering, model evaluation).
- Practical experience with LangChain and/or Haystack frameworks for building LLM-powered applications.
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