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Senior Engineer – AI Delivery

Elm · Riyadh

New
🇬🇧 English
Vision-language models Retrieval Batching Quantization Caching

Job description

About the role

The Senior Engineer – AI Delivery will design, build, evaluate and deploy applied AI solutions, including large‑language‑model (LLM) workflows, vision‑language models and autonomous agents. The role selects the simplest effective design, grounds decisions in rigorous evaluation, and ensures solutions meet latency, cost, safety and governance requirements while collaborating with product, engineering, infrastructure and ODC teams.

Key responsibilities

  • Scope AI use cases, translate business problems into well‑defined AI tasks, and prototype solutions with LLMs and vision‑language models.
  • Design agent architectures, including MCP server integration, memory handling and sub‑agent coordination for multi‑step tasks.
  • Engineer model context through retrieval, prompt structuring and compaction, and fine‑tune models (SFT, LoRA) when needed.
  • Build evaluation datasets and automated benchmarking harnesses, defining metrics tied to real success criteria such as accuracy, latency and cost.
  • Develop inference pipelines, selecting serving setups, batching, quantization and caching to meet production performance targets.

Required profile

  • Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Computer Engineering or a related field.
  • 2–4 years of experience in applied AI/ML engineering, LLM application development, agentic systems, model evaluation or AI solution deployment.
  • Professional certifications in Machine Learning, Generative AI, MLOps or Cloud Computing are a plus.

Required skills

  • Large Language Models (LLMs) and vision‑language model development.
  • Agent architecture design and MCP server integration.
  • Prompt engineering, retrieval and context compaction.
  • Model fine‑tuning techniques (SFT, LoRA) and task‑specific heads.
  • Evaluation dataset creation and automated benchmarking.
  • Inference pipeline engineering (batching, quantization, caching).
  • Implementation of AI safety guardrails and access control mechanisms.

Questions fréquentes

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Source : ats:successfactors

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Published 8 hours ago

Expires 1 month from now

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Elm

Riyadh