AI, Engineer

Master Works is seeking an AI Engineer with strong generative AI prototyping experience to rapidly turn ideas into working AI-powered demos, proofs of concept, and early-stage products.

The role sits between research and production engineering — combining hands-on use of large language models, multimodal models, retrieval-augmented generation, and agent frameworks to validate feasibility, explore new capabilities, and de-risk feature delivery.

The role is central to accelerating AI innovation across the data, analytics, and intelligence portfolio, with a particular emphasis on bilingual (Arabic/English) AI use cases.

Rapidly design and build generative-AI prototypes and proofs of concept — from initial idea to working demo — using modern LLM APIs, open-source models, and prototyping frameworks.

Design and implement retrieval-augmented generation (RAG) pipelines, including document ingestion, chunking strategies, embedding generation, vector storage, and retrieval orchestration.

Build agentic systems that use tool/function calling, planning, and multi-step reasoning to automate complex tasks; integrate AI agents with internal APIs, databases, and external services.

Engineer prompts, system instructions, and structured-output schemas; iterate on prompt design through measurable evaluation rather than guesswork.

Work with multimodal AI capabilities — text, vision, speech (STT/TTS), and document understanding — and combine them into coherent end-to-end AI pipelines.

Build and run AI evaluation frameworks (offline benchmarks, LLM-as-judge, golden-set regression testing) to measure quality, accuracy, latency, and cost across model and prompt variants.

Adapt and fine-tune open-source models (LoRA, QLoRA, full fine-tuning) when proprietary or domain-specific behaviour is required, especially for Arabic-language tasks.

Apply responsible-AI practices — safety filtering, hallucination mitigation, prompt-injection defence, PII handling, and bias awareness — especially for sensitive or regulated environments.

Hand off mature prototypes to production engineering teams, providing clear documentation, evaluation results, deployment notes, and migration guidance.

Demo prototypes and AI capabilities to internal stakeholders and clients; explain technical concepts, limitations, and risks to non-technical audiences.

Track and evaluate fast-moving developments in the generative-AI ecosystem (new models, tools, techniques, and providers) and recommend adoption where they materially improve outcomes.

Collaborate cross-functionally with product, design, data, and software engineering teams to shape AI features end-to-end and ensure they deliver real user value.

Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline.

Postgraduate qualification (MSc/PhD) in Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field is preferred.

Recognised certifications in machine learning, deep learning, generative AI, or cloud AI platforms (AWS, Azure, GCP) are a strong plus.

3–5+ years of AI/ML engineering experience, with at least 1–2 years hands-on building generative-AI applications using modern LLMs.

Demonstrable portfolio of shipped prototypes or production AI features — ideally including RAG systems, AI agents, or multimodal pipelines.

Hands-on experience with leading LLM APIs (e.

g., Anthropic Claude, OpenAI GPT, Google Gemini) and open-source model serving (e.

g., Hugging Face, vLLM, Ollama).

Strong Python proficiency, including modern AI libraries such as PyTorch, Hugging Face Transformers, LangChain or LlamaIndex, and standard data tooling.

Working experience with embedding models, vector databases (e.

g., Pinecone, Weaviate, Qdrant, FAISS), and retrieval evaluation techniques.

Familiarity with rapid-prototyping tools (Streamlit, Gradio, Jupyter), API integration, and lightweight web back-ends.

designing AI evaluation harnesses (offline benchmarks, LLM-as-judge, golden sets) and using observability/tracing tools for AI systems.

Exposure to fine-tuning techniques (LoRA, QLoRA, instruction tuning) and to Arabic-language NLP is strongly preferred.

Working knowledge of containerisation (Docker), version control (Git), and cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a strong plus.

Excellent leadership and team development skills.

Strong bias for action and a rapid-prototyping mindset; able to ship a working demo in days, not months.

Comfortable working under uncertainty — rapidly forming hypotheses, running experiments, and iterating based on evidence.

Strong analytical and problem-solving skills, with the discipline to evaluate AI behaviour quantitatively rather than relying on intuition alone.

Excellent verbal and written communication, with the ability to demo AI capabilities and explain technical concepts, limitations, and risks to non-technical audiences.

Curiosity and continuous-learning mindset; actively follows the rapidly evolving generative-AI landscape and brings new ideas back to the team.

Collaborative team player; comfortable in cross-functional teams of researchers, software engineers, designers, and client business owners.

Cultural awareness and sensitivity, particularly when designing AI experiences for Arabic-speaking users in regional contexts.