AI Product, Senior Engineer (LLM & Agentic AI)

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.