Director, AI & Agentic Engineering

OpenTalent; California (MO); On-site; USD 180,000 - 230,000; Full time

Job summary

Artefact is launching in the US with offices in NYC and Los Angeles. We seek a senior leader to head AI & ML engineering efforts, shaping production-grade AI solutions—from traditional ML models to LLM-powered applications—and to define context engineering, agent harnesses, and tooling practices within a cross-functional team.

You will partner with clients and business stakeholders, contribute to pre-sales work shaping proposals and solutions, and report to senior AI/tech leadership on strategy

Qualifications

• Leads design and delivery of production AI systems from ML models to LLM apps.
• Defines context engineering practices (prompts, memory, tools) for reliable models.
• Shapes solutions with clients and business stakeholders and supports pre-sales activity.

Responsibilities

• Lead AI/ML solution architecture for production systems.
• Define and standardize context engineering and RAG architectures.
• Develop robust agent harnesses with orchestration and observability.

Skills

AI/ML Architecture

Context Engineering

Agent Harnesses

Pre-sales support

Tools

Vector stores

RAG architectures

Prompt design

Job description

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.

Find out more at artefact.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.