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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director, AI & Data Science - **Company:** Artefact - **Location:** New York, United States - **Experience:** Experienced - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Memory Management, Machine Learning, Performance Tuning, Tensorflow, Software Deployment, Pytorch, Large Language Models, Multi-Agent Systems, Deep Learning, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Software Library - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/a4be1890-8119-4f9f-8e52-c8777fa0cd77 ## 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. ## Related Videos - [Beyond Chatbots: How to build Agentic AI systems](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)