Nabeh, a subsidiary of Master Works, delivers AI-powered solutions that help organizations automate operations, boost efficiency, and drive innovation. Its expertise spans computer vision, LLM applications, speech-to-text, data intelligence, and low-code AI solutions.
Nabeh is looking for an AI Product Engineer (LLM & Agentic AI) to develop and deploy production-ready AI applications using LLMs, RAG, and Agentic AI, contributing to Nabeh's AI products and enterprise solutions.
Key Responsibilities
LLM Application Development
• Design and develop enterprise AI applications powered by proprietary and open-source LLMs.
• Build advanced RAG pipelines for ingestion, chunking, embeddings, indexing, retrieval, reranking, and grounded responses.
• Develop AI agents capable of tool calling, multi-step task execution, and integration with enterprise systems.
• Implement multi-agent and workflow-based AI solutions with reliable execution and error handling.
• Develop structured output pipelines, prompt templates, and context-management strategies.
• Build conversational AI capabilities supporting multilingual interactions, particularly Arabic and English.
LLM Platform & Inference Engineering
• Integrate multiple AI providers through OpenAI-compatible APIs.
• Deploy and serve open-source LLMs using inference frameworks such as vLLM.
• Work with cloud-hosted and on-premises inference infrastructure.
• Optimize inference latency, throughput, concurrency, token usage, and cost.
• Implement model routing, fallback mechanisms, request logging, usage tracking, and API integration.
• Support deployments on NVIDIA GPUs and other AI accelerators, including Qualcomm Cloud AI platforms.
Production AI Engineering
• Build reliable backend services using Python and frameworks such as FastAPI.
• Design RESTful APIs, asynchronous workers, streaming responses, and event-driven integrations.
• Work with SQL and vector databases such as PostgreSQL, pgvector, Qdrant, or Milvus.
• Integrate AI services into enterprise workflows, knowledge systems, and third-party applications.
• Containerize applications using Docker and support Kubernetes-based deployments.
• Implement observability, automated testing, CI/CD, and production monitoring.
AI Quality, Evaluation & Governance
• Develop evaluation pipelines measuring answer quality, grounding, hallucination rates, latency, and cost.
• Implement prompt-injection defenses, access controls, PII protection, and content guardrails.
• Support secure AI deployments aligned with enterprise privacy requirements.
• Evaluate and benchmark new LLMs, embeddings, reranking models, and inference approaches.
• Troubleshoot production issues across application, model, and inference layers.
Product Ownership & Collaboration
• Collaborate with product managers to convert requirements into functional AI capabilities.
• Own assigned features from technical design and implementation to deployment and monitoring.
• Deliver maintainable, reusable components rather than isolated POCs.
• Participate in architecture reviews, technical documentation, code reviews, and product demonstrations.
Requirements
REQUIRED TECHNICAL SKILLS/EXPERINCE
• 3–6 years of relevant experience.
• Strong programming skills in Python.
• Hands-on experience integrating and developing applications using LLM APIs or open-source models.
• Proven experience with RAG architectures and vector databases.
• Experience with at least one agent or LLM orchestration framework such as LangGraph, LangChain, or LlamaIndex.
• Strong API development experience using FastAPI or similar frameworks.
• Familiarity with Docker, Linux, Git, and production deployment workflows.
• Experience with SQL databases and asynchronous backend processing.
• Strong understanding of model parameters, tokenization, context windows, embeddings, and LLM evaluation.
• Ability to independently develop production-ready AI features.
• Fluency in both Arabic and English, with strong verbal and written communication skills.
PREFERRED QUALIFICATIONS
• Experience with vLLM, Hugging Face Transformers, LiteLLM, or other inference frameworks.
• Experience building enterprise AI agents with tool calling, memory, and workflow orchestration.
• Knowledge of speech AI pipelines including STT, TTS, and real-time voice agents.
• Experience optimizing Arabic LLM applications.
• Familiarity with model quantization, GPU utilization, and inference performance tuning.
• Experience working with NVIDIA or Qualcomm AI acceleration infrastructure.
• Knowledge of Kubernetes, Redis, Kafka, or distributed application architectures.
• Experience with AI security, prompt-injection prevention, and PII filtering.