Offer summary

Qualifications:

Background in Python AI/ML Engineering or Backend Engineering with GenAI applications., Solid understanding of modern Python coding, testing, and debugging techniques., Hands-on experience with LLMs and Agentic architectures using LangChain or similar tools., Strong experience with cloud platforms like GCP or AWS and API design frameworks..

Key responsibilities:

Experiment with POCs to solve real-world problems using Large Language Models.

Collaborate on AI-driven projects with engineers, product managers, and AI specialists.

Build and deploy LLM-based solutions and design APIs using Python and Flask.

Manage AI applications on Google Cloud Platform services and optimize data pipelines.

Job description

AI Engineer

About Elsewhen:

Elsewhen, a London-based consultancy, designs digital products and services for clients like Spotify, Google, Inmarsat, and Zego. Over the past decade, we have built a workplace prioritising impact, drive, openness, and friendliness. We value outcomes over hours and agility over rigid processes. If you’re a Python Engineer with an interest in AI, this is a great opportunity to gain hands-on GenAI experience while working on real-world projects, with the support and training needed to grow your GenAI skills.

We’re looking for an AI engineer to join our biggest project and help bring LLM capabilities into our existing product. You will dive into AI engineering, building LLM powered systems and exploring multi-agent architectures within an enterprise organisation.

Work Environment:

Remote-first setup: Fully remote work with the option to use a WeWork membership for those who prefer occasional office access.
Join our team:https://www.elsewhen.com.

What You’ll Do:

Experiment with POCs to find solutions for real-world problems using Large Language Models.
Collaborate on AI-driven projects, working alongside engineers, product managers and AI specialists while maintaining clear documentation
Build and deploy Agentic LLM-based solutions with LangGraph.
Familiar with different multi agent system patterns
Build and deploy LLM-based solutions using RAG
Design and optimise APIs using Python and Flask to serve AI solutions.
Deploy and manage AI applications on Vertex and other Google Cloud Platform (GCP) services using Terraform for infrastructure as code.
Build and optimise data pipelines for vector search and knowledge retrieval using Vector databases and embedding models.

What We’re Looking For:

Background in Python AI/ML Engineering or Backend Engineering with GenAI applications
Solid understanding of the Python standard library and modern Python coding, testing, debugging and automation techniques
Hands-on experience building solutions using LLMs and Agentic architectures with LangChain, LlamaIndex, or LangGraph.
Experience working with vector databases for embedding and indexing.
Strong experience with cloud platforms such as Google Cloud Platform (GCP) or Amazon Web Services (AWS)
Strong experience with API design and frameworks like FastAPI or Flask
Solid experience with relational databases and SQL
Interest in expanding your knowledge into GenAI and machine learning
Excellent communication skills and the ability to work well in a collaborative team environment

Nice to have: