Director, AI & Data Science

Job summary

Responsibilities

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?

• AI & ML Solution Architecture: Leading the design, build, and optimization of production AI systems — classical machine learning models, LLM applications, and agentic systems —…
• Context Engineering: Defining and standardizing context engineering practices — prompt and system design, RAG architectures, vector stores, memory management, and tool/function…
• Harness Engineering: Directing the build of robust agent harnesses — orchestration layers, evaluation frameworks, guardrails, and observability — that make LLM systems reliable…
• Fine-Tuning Pipelines: Leading the design and operation of fine-tuning and model adaptation pipelines — training data curation, supervised fine-tuning, evaluation, and deployment…
• 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…

What they're looking for

• 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…
• Expertise in generative AI/LLM engineering (context engineering, agent harnesses, RAG, and fine-tuning) and in classical machine learning modeling, with proven production…
• 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…
• Experience building fine-tuning pipelines end to end: training data curation, supervised fine-tuning, evaluation, and deployment.

Tech stack

• RAG
• AWS
• scikit-learn
• LangGraph
• LangChain
• PyTorch
• TensorFlow