Job Description
Anypear is a headhunting and recruitment agency connecting New Zealand and Australian businesses with top overseas talent.
We are looking for an insightful and innovative Applied AI Engineer to help our client, a UK based YouTube Media Company, put data and technology at the heart of decision making. Reporting directly to the Director of Data and Technology, you’ll be responsible for building agentic workflows and data pipelines to optimise YouTube creators’ performance and growth.
Responsibilities
- Design, test and ship production-grade agentic workflows in Python (ideally using LangGraph).
- When required, contribute to the development, testing and maintenance of fast, reliable data pipelines.
- AI/ML
- Knowledge of applying LLMs and developing, testing and deploying agentic systems in Python (ideally LangGraph)
- Deep knowledge of Python and relevant libraries (LangChain, LangGraph, FastAPI, Pydantic, etc)
- Data Engineering
- Knowledge of designing, building, testing, deploying and maintaining data pipelines using Airflow (and ideally Cloud Composer)
- DevOps
- Good understanding of DevOps processes (Docker, CI/CD in GitHub actions, deploying in GCP)
- Good understanding of GCP (especially Cloud Composer, Cloud Run, Google App Engine)
- Deep knowledge of version control (GitHub)
What your first 6 months may look like:
- Launch an internal agentic tool, aimed at improving efficiency and quality.
- Improve the speed and reliability of our data pipelines.
Requirements
- Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics or STEM subject.
- Experience - Minimum of 3 years in an AI/ML/Data Engineer position.
- English fluency
- Communication skills (especially technical points to non-technical audience)
- Ability to quickly & effectively prototype
- Keen interest in applying AI to business problems
Nice to have
- Developing/deploying/testing APIs & RAG systems.
- Experience in the digital content or media sector (ideally YouTube).
- Experience mentoring junior team members.
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