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- Tasks: Build and deploy innovative AI features using Python and large language models.
- Company: Join a forward-thinking team focused on cutting-edge AI technology.
- Benefits: Enjoy a fully remote role with competitive pay and potential for contract extension.
- Why this job: Be at the forefront of AI development, working on real-world applications that make an impact.
- Qualifications: Strong Python skills and experience with LLMs are essential; bonus for model optimization knowledge.
- Other info: This is a 12-month contract with a rate of £500–£600/day, outside IR35.
The predicted salary is between 100000 - 120000 £ per year.
Contract LLM Engineer – Python / AI
Location: Remote (UK/EU preferred)
Rate: £500–£600/day (Outside IR35)
Length: 12 months (Possibility of extension)
Start: ASAP
I\’m on the lookout for a Contract LLM Engineer to help build and deploy cutting-edge AI features using Python and large language models. You’ll work on fine-tuning, integrating, and scaling LLMs for real-world applications.
Key Skills:
Strong Python engineering background
Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain)
Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone)
Cloud (AWS/GCP), CI/CD, Docker
Bonus: Knowledge of model optimization, quantization, or open-source contributions.
If interested send your CV to
LLM Engineer - 12 month contract - Fully remote employer: Opus Recruitment Solutions
Contact Detail:
Opus Recruitment Solutions Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land LLM Engineer - 12 month contract - Fully remote
✨Tip Number 1
Make sure to showcase your experience with large language models in your conversations. Discuss specific projects where you've fine-tuned or integrated LLMs, as this will demonstrate your hands-on expertise.
✨Tip Number 2
Familiarise yourself with the latest trends and advancements in AI and Python engineering. Being able to discuss recent developments or tools like Hugging Face or LangChain can set you apart during interviews.
✨Tip Number 3
Network with professionals in the AI and Python communities. Engaging in discussions on platforms like LinkedIn or GitHub can help you gain insights and potentially get referrals for the role.
✨Tip Number 4
Prepare to discuss your experience with cloud services and CI/CD practices. Highlighting your familiarity with AWS or GCP, along with Docker, will show that you're ready to hit the ground running in a remote environment.
We think you need these skills to ace LLM Engineer - 12 month contract - Fully remote
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your strong Python engineering background and any relevant experience with large language models. Include specific projects or roles where you've fine-tuned models or worked with tools like Hugging Face or OpenAI.
Showcase Relevant Skills: In your application, emphasise your experience with model fine-tuning, RAG pipelines, and vector databases such as FAISS or Pinecone. Mention any cloud experience (AWS/GCP) and CI/CD practices you are familiar with.
Include a Cover Letter: Write a concise cover letter that explains why you're interested in the role and how your skills align with the company's needs. Highlight any bonus skills, such as knowledge of model optimization or contributions to open-source projects.
Proofread Your Application: Before submitting, carefully proofread your CV and cover letter for any errors or typos. A polished application reflects your attention to detail and professionalism, which is crucial for a technical role.
How to prepare for a job interview at Opus Recruitment Solutions
✨Showcase Your Python Skills
Make sure to highlight your strong Python engineering background during the interview. Be prepared to discuss specific projects where you've used Python, especially in relation to AI and LLMs.
✨Demonstrate LLM Experience
Discuss your experience with large language models, particularly any work you've done with frameworks like Hugging Face or OpenAI. Be ready to explain how you've fine-tuned models or integrated them into applications.
✨Familiarity with Tools and Technologies
Be knowledgeable about the tools mentioned in the job description, such as RAG pipelines, vector databases like FAISS or Pinecone, and cloud services like AWS or GCP. Prepare examples of how you've used these technologies in past projects.
✨Prepare for Technical Questions
Expect technical questions related to model optimization, quantization, and CI/CD processes. Brush up on these topics and be ready to discuss any relevant open-source contributions you may have made.
