Artificial Intelligence Engineer

About the role

The Artificial Intelligence Engineer will join Talent Bridge’s engineering team to design, develop, and deploy production‑grade AI solutions for the GoldenTik multi‑vendor e‑commerce platform. Based in Amman, this full‑time on‑site position focuses on generative AI, large language models, computer vision, OCR, speech technologies and intelligent recommendation systems.

Key responsibilities

• Design, train, fine‑tune and deploy machine‑learning and deep-learning models, including LLM‑powered applications.
• Build Retrieval‑Augmented Generation pipelines using embeddings, vector databases and semantic retrieval.
• Develop NLP solutions for classification, summarisation, translation, intent detection and conversational AI.
• Create computer‑vision pipelines for object detection, product recognition, visual similarity and moderation.
• Implement OCR workflows to extract structured data from invoices, receipts and product packaging.
• Develop speech‑to‑text, text‑to‑speech and voice‑AI components for multilingual voice assistants.
• Integrate AI services from providers such as OpenAI, Anthropic, Google Gemini and Hugging Face.
• Build scalable AI APIs and micro‑services, optimise inference cost, latency and GPU/CPU utilisation.
• Monitor production models, conduct performance testing and continuously improve system reliability.

Required profile

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning or related field, or equivalent practical experience.
• Strong Python programming skills and solid software‑engineering fundamentals.
• Hands‑on experience with PyTorch or TensorFlow and modern transformer architectures.
• Proven experience building and deploying large language models and generative AI solutions.
• Familiarity with vector‑database technologies and Retrieval‑Augmented Generation.
• Experience with computer‑vision frameworks and OCR tools.
• Knowledge of speech‑to‑text and text‑to‑speech technologies.
• Experience with Docker, Linux, cloud environments and GPU‑accelerated workloads.
• Understanding of MLOps practices, model versioning and monitoring.

Required skills

• Python
• PyTorch, TensorFlow
• Large Language Models, Prompt engineering
• OpenAI, Anthropic, Google Gemini, Hugging Face APIs
• Retrieval‑Augmented Generation, Embeddings, Vector databases (Qdrant, Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, OpenSearch)
• NLP, Transformer architectures
• Computer Vision, OpenCV, YOLO, torchvision, Detectron
• OCR (Tesseract, PaddleOCR, EasyOCR)
• Speech‑to‑Text (Whisper), Text‑to‑Speech, Voice AI
• SQL, NoSQL databases
• REST APIs, WebSockets, Microservices
• Docker, Linux, Cloud platforms
• GPU workloads, Model deployment, Inference optimisation
• MLOps, FastAPI, Kubernetes
• LangChain, LangGraph, LlamaIndex, vLLM
• LoRA, QLoRA, PEFT, Model fine‑tuning, Quantisation