At a Glance
- Tasks: Build innovative graph-based AI solutions and tackle complex data challenges.
- Company: Exciting AI scale-up in the UK focused on secure AI systems.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and security.
- Why this job: Join a cutting-edge team and make a real impact in the AI landscape.
- Qualifications: Experience with Python, Neo4j, Cypher, and NLP tasks is essential.
The predicted salary is between 60000 - 80000 £ per year.
SR2 is working with an exciting AI scale-up in the UK, focused on delivering secure AI systems. The successful candidate will take customer use cases from scope through delivery, focusing on Python, FastAPI, and strong knowledge graph expertise.
Responsibilities include:
- Building graph-based retrieval solutions
- Working with complex data
Ideally, applicants should possess hands-on experience with Neo4j and Cypher, as well as a background in NLP tasks. A collaborative approach in regulated environments will be advantageous.
Graph-Driven AI Engineer: LLMs, Retrieval & Knowledge Graphs in England employer: SR2 | Socially Responsible Recruitment | Certified B Corporation™
Join a dynamic AI scale-up in the UK that prioritises innovation and security in AI systems. With a collaborative work culture, employees are encouraged to grow their skills through hands-on projects and mentorship, particularly in cutting-edge technologies like knowledge graphs and NLP. The company offers a supportive environment where your contributions directly impact customer success and the future of AI.
Contact Details:
SR2 | Socially Responsible Recruitment | Certified B Corporation™ Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Graph-Driven AI Engineer: LLMs, Retrieval & Knowledge Graphs in England
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We think you need these skills to ace Graph-Driven AI Engineer: LLMs, Retrieval & Knowledge Graphs in England
Some tips for your application 🫡
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