Director, AI Engineering

About the job Do you think like a management consultant, thrive in a startup environment, and can’t stop thinking about the intersection of data, technology, and marketing? With over 2000 employees, offices on five continents, and world-class clients like Samsung, L’Oreal, and Mattel, Artefact is a consulting firm that transforms data into value and business impact. We’ve recently launched in the US with offices located in NYC and Los Angeles, and we want you to join us as an integral part of our founding team!

Who We Are

Founded and headquartered in Paris, Artefact is a next-generation consulting firm, specializing in data, analytics & AI consulting, dedicated to transforming data into business impact across the entire value chain of organizations. We are proud to say that we help our clients grow their data and digital capabilities, and that we’re also growing in parallel. We have 2000 employees across 36 offices who are focused on accelerating digital transformation.

Our state-of-the-art data technologies, lean AI agile methodologies, and cohesive teams of the finest business consultants, data analysts, data scientists, data engineers, and digital experts are all dedicated to bringing extra value to every client. We design data-based solutions to meet our clients' specific needs, always conceived with a business-centric approach and delivered with tangible results. Our data-driven services are built upon the deep AI expertise we've acquired with our 1000+ client base around the globe.

com . What you will be doing You will lead a team of AI & machine learning engineers and managers, driving the design and delivery of production-grade AI solutions — from classical machine learning models to LLM-powered applications — and the pipelines that power them. You’ll bring senior technical judgment to architecture and model decisions, partner closely with clients and business stakeholders — including hands-on pre-sales work shaping proposals and solution designs — and define how context engineering, agent harnesses, and fine-tuning practices get embedded into every solution, while reporting into senior AI/technology leadership on strategy and priorities.

AI & ML Solution Architecture: Leading the design, build, and optimization of production AI systems — classical machine learning models, LLM applications, and agentic systems — ensuring scalability, reliability, and cost-efficient inference. Context Engineering: Defining and standardizing context engineering practices — prompt and system design, RAG architectures, vector stores, memory management, and tool/function calling — so models receive the right information at the right time. Harness Engineering: Directing the build of robust agent harnesses — orchestration layers, evaluation frameworks, guardrails, and observability — that make LLM systems reliable, safe, and measurable in production.

Fine-Tuning Pipelines: Leading the design and operation of fine-tuning and model adaptation pipelines — training data curation, supervised fine-tuning, evaluation, and deployment — to specialize models for client use cases. Platform Stack: Architecting and deploying solutions on Google Gemini Enterprise and Vertex AI as the primary stack, applying working knowledge of Microsoft AI Foundry and AWS Bedrock where client contexts require. Team Leadership: Managing, mentoring, and developing a team of AI & ML engineers; setting technical standards and fostering best practices and knowledge sharing.

Pre-Sales & Business Development: Supporting pre-sales activities — scoping engagements, building demos and proofs of concept, and presenting solution architectures to prospective clients alongside account teams. Machine Learning Modeling: Overseeing the development of classical and modern ML models — predictive modeling, forecasting, recommendation, and deep learning — choosing the right technique for each business problem, LLM or not.