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# Director, AI & Data Science

- **Company:** Artefact
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $200,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Training Data, Artificial Intelligence, Amazon Web Services, Memory Management, Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Deep Learning, Agentic-AI, AI Platforms, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Claude, Google Gemini, Agent SDK, Software Library
- **Published:** November 5, 2026
- **Apply:** https://www.careerboard.com/us/en/find-jobs-in-United-States/-5E5E154D1D244635F4/

## About the Role

* The ideal candidate has a substantial Data Science and machine learning background with 8+ years of experience, including at least 2-3 years working on LLM architecture, agentic design, and harness & context engineering.
 * Expertise in generative AI/LLM engineering (context engineering, agent harnesses, RAG, and fine-tuning) and in classical machine learning modeling, with proven production deployments.
 * Master's degree (or higher) in computer science, engineering, statistics/mathematics, or a related field.
 * Hands-on command of core machine learning libraries (scikit-learn, XGBoost, etc.), agentic SDKs (LangGraph/LangChain, Google ADK, Claude Agent SDK), and fine-tuning frameworks (PyTorch, TensorFlow).
 * Experience building fine-tuning pipelines end to end: training data curation, supervised fine-tuning, evaluation, and deployment.
 * Solid grasp of AI system design: ML model lifecycle (MLOps), agents, tool use, evaluation harnesses, guardrails, and observability.
 * Deep experience with Google Gemini Enterprise/Vertex AI; basic working knowledge of Microsoft AI Foundry and AWS Bedrock.
 * Experience leading and growing engineering teams, and supporting pre-sales: proposals, demos, and solution scoping with clients.
 * Excellent communication skills and comfort collaborating across teams and with stakeholders.
 * Strong business acumen with an interest in business-facing work.
 * Adaptability and a start-up mentality to thrive in a dynamic environment.

Preferred:

 * Google Gemini Enterprise ecosystem (Vertex AI, Agent Builder) as the primary stack; basic knowledge of Microsoft AI Foundry and AWS Bedrock

## Description

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.
 * Contributing to AI Strategy: Partnering with senior leadership to shape GenAI architecture direction, tooling decisions, and platform roadmap within your area.