Join a fast-growing scale-up as an AI Engineer with expertise in open-source software, API development, and LLM fine-tuning. Develop and maintain scalable RESTful & GraphQL APIs, design and implement Retrieval-Augmented Generation (RAG) pipelines, and fine-tune LLMs for domain-specific performance.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
AI Engineer
Salary: £70,000
Location: Fully remote
We are seeking an AI Engineer with a strong open-source software background to join a fast-growing scale-up. They offer fully remote employment and have a team of engineers dedicated to developing AI-powered search and Retrieval-Augmented Generation (RAG) pipelines within DataStax-based architecture.
This role is perfect for someone with expertise in Open-source, API development, fine-tuning LLMs, and integrating AI into scalable applications. If you thrive in an AI-driven development environment, enjoy working on cutting-edge retrieval models, and have a passion for optimising LLM performance, we want to hear from you.
Key Responsibilities:
- Develop and maintain scalable RESTful & GraphQL APIs to interact with LLMs and AI-powered search systems.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines using open-source tools like LangChain, Langflow, and DataStax Vector Search.
- Fine-tune LLMs (Hugging Face, OpenAI, Cohere, or similar) to improve response quality and domain-specific performance.
- Optimise embedding models for better semantic search and document retrieval.
- Collaborate with AI/ML, data engineering, and DevOps teams to ensure seamless system integration.
- Monitor API performance and AI search efficiency, implementing optimizations where necessary.
- Stay up to date with advancements in LLM fine-tuning, RAG methodologies, and vector search techniques.
Essential Skills & Experience:
- Programming: Strong proficiency in Opensource.
- API Development: Experience with FastAPI, Flask (Python) OR Express.js for microservices.
- RAG Development: Hands-on experience building retrieval-augmented AI search solutions.
- LLM Fine-Tuning: Experience fine-tuning models for domain-specific performance and optimizing inference speed.
- Passionate about AI and Open Source: Demonstrated involvement in open-source projects and a passion for AI and its applications.
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