AI Specialist- Native Arabic Speakers

Responsibilities

• Architect and deliver production-ready LLM applications including conversational agents, intelligent assistants, and RAG pipelines with advanced retrieval and re-ranking
• Build multi-agent systems using strong Lang Chain, Lang Graph, Auto Gen, or Crew AI
• Apply prompt engineering techniques to optimize model performance
• Integrate LLM solutions with enterprise systems via REST APIs and event-driven architectures
• Design agentic workflows with tool use, memory management, planning loops, and human-in-the-loop controls
• Build AI-driven automation pipelines across structured and unstructured enterprise data sources
• Design and manage vector stores Pinecone, Weaviate, Qdrant, FAISS, or PG Vector with semantic and hybrid search strategies
• Maintain knowledge bases powering enterprise AI applications, ensuring accuracy and freshness of indexed content
• Mentoring: guide and review the work of associate-level engineers; contribute to reusable frameworks and internal engineering standards
• Lead client workshops, technical discovery sessions, and proof-of-concept demonstrations; produce clear solution design documentation
• Evaluate emerging LLMs, multimodal models, and local inference runtimes; prototype new tools and share findings with the team
• Contribute to Beinex thought leadership through internal knowledge-sharing sessions, technical write-ups, or industry presentations

• 5+ years of professional experience in AI Engineering, Machine Learning, or Applied NLP, with at least 2 years focused on LLMs and Generative AI
• Strong proficiency in Python and experience with Hugging Face Transformer Library, Lang Chain, Lang Graph, FastAPI, and Py Torch
• Deep practical knowledge of LLM fine-tuning, prompt engineering, RAG pipeline design, and agentic AI development
• Hands-on experience with vector databases such as Pinecone, Weaviate, Qdrant, FAISS, or PG Vector, along with embedding models
• Proven ability to deploy and product ionize AI solutions in AWS, Azure, or GCP using Docker and Kubernetes
• Experience integrating AI solutions with enterprise platforms through REST APIs, webhooks, or event-driven architectures
• Strong understanding of responsible AI principles, including hallucination mitigation, output evaluation, content safety, and model governance
• Excellent communication skills with the ability to explain complex technical concepts to both technical and business stakeholders
• Demonstrated ability to independently lead AI projects from solution design through production deployment
• Preferred
  
  Certifications:
  
  AWS Certified Machine Learning – Specialty, Azure AI Engineer, GCP Professional Machine Learning Engineer, or equivalent cloud AI/ML certification;
  Deep Learning Specialization; LLM Engineering or Generative AI certifications from recognized platforms;
  Certified Kubernetes Application Developer (CKAD) is an added advantage