AI Innovation Engineer

The AI Innovation Engineer is a critical member of Master Works’ Innovation Department, responsible for driving the design and delivery of AI-powered PoCs and MVPs that solve real business challenges. This role requires a unique blend of engineering excellence, creativity, and applied research mindset to translate cutting-edge AI capabilities -especially those built on large language models (LLMs)-into functional and validated digital solutions. You will lead AI initiative execution across a variety of domains by leveraging pre-trained models, advanced orchestration techniques, and innovative workflows. Your primary development stack will be Python and modern AI libraries. You will also bridge innovation delivery by building lightweight APIs or frontends using Node.js and React when needed to complete a prototype or MVP.

Requirements

• Design and deliver AI-driven PoCs and MVPs that directly support strategic innovation goals across Master Works and its subsidiaries.
• Develop full-stack AI systems with Python at the core—using frameworks like FastAPI, Transformers, LangChain, Haystack, and PyTorch—to implement AI pipelines and intelligent service components.
• Integrate and orchestrate LLMs using : - Retrieval-Augmented Generation (RAG) - - - - Knowledge-Augmented Generation (KAG) Model Context Protocol (MCP) Multi-agent orchestration and A2A workflows Prompt engineering and fine-tuning where applicable
• Interface with LLM providers (OpenAI, Claude, Groq, Cohere, open-source models) through APIs and SDKs, optimizing model utility and cost-efficiency.
• Design and manage vector-based search architectures using tools like ChromaDB, Weaviate, Pinecone, or FAISS.
• Prototype interaction layers, visual interfaces, and lightweight dashboards using Node.js and React, especially for showcasing PoC / MVP deliverables.
• Collaborate with business stakeholders to shape and prioritize AI use cases, ensuring alignment with internal innovation streams and subsidiary needs.
• Evaluate, benchmark, and refine AI models for accuracy, performance, and business relevance.
• Create internal documentation, reusable components, and technical guidelines to support scaling and knowledge sharing.
• Stay up to date with emerging AI trends and continuously integrate relevant new methods into practice.

Required Qualifications :

Preferred Qualifications

Core Competencies :