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- Tasks: Join a startup to develop and deploy cutting-edge AI applications using Large Language Models.
- Company: Be part of an innovative startup pushing the boundaries of Data and AI.
- Benefits: Enjoy a flexible work environment with opportunities for growth and impact.
- Why this job: Contribute to groundbreaking projects and be part of a founding team shaping the future.
- Qualifications: Bachelor’s or Master’s in Computer Science, 3+ years in machine learning, and LLM expertise required.
- Other info: Salary is negotiable; candidates must have the right to work in the UK.
The predicted salary is between 36000 - 60000 £ per year.
UpViVA have partnered with an exciting startup who are pushing the boundaries of Data and AI. They are looking for an experienced AI Engineer with expertise in Large Language Models (LLMs) to join our team and work on the development of advanced generative AI applications. You will be responsible for fine-tuning, customising, and integrating state-of-the-art LLMs to drive real-world impact across our clients’ products and platforms. This role is ideal for someone who wants to be part of a founding team.
The Role:
- Develop and deploy LLM-based solutions tailored to specific business needs (e.g., chatbots, summarization, content generation, semantic search)
- Fine-tune and customize pre-trained LLMs for targeted applications
- Conduct prompt engineering, few-shot learning, and optimise model performance
- Build pipelines for scalable model training, inference, and evaluation
- Integrate LLMs into end-user applications via APIs or custom deployments
- Analyse outputs and optimize performance for accuracy, latency, cost, and safety
- Stay on top of the latest research in LLMs, including open-source developments and benchmarks
- Work closely with product and engineering teams to bring LLM-powered features from concept to production
The Person:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field
- 3+ years of experience in machine learning or NLP, with significant hands-on experience with LLMs
- Proficiency with LLM frameworks such as Hugging Face Transformers, OpenAI API, LangChain, or similar
- Experience fine-tuning large transformer models or implementing retrieval-augmented generation systems
- Strong Python programming skills and familiarity with ML libraries (e.g., PyTorch, TensorFlow)
- Knowledge of prompt engineering best practices and prompt optimization
- Understanding of LLM evaluation methods, including human-in-the-loop and automated metrics
- Familiarity with deploying LLMs in cloud or containerised environments
The salary for this opportunity is negotiable on experience. Reach out for full information - submit your CV for a confidential call, or contact carol@vivatechtalent.com.
All candidates must have the right to work in the UK - VISA sponsorship is not available.
Artificial Intelligence Engineer employer: Job Traffic
Contact Detail:
Job Traffic Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Make sure to showcase your hands-on experience with Large Language Models (LLMs) during any networking opportunities. Engage in discussions about your projects and the specific frameworks you've used, like Hugging Face Transformers or OpenAI API, to demonstrate your expertise.
✨Tip Number 2
Stay updated on the latest research and developments in LLMs. Follow relevant blogs, attend webinars, and participate in online forums. This knowledge will not only help you in interviews but also show your passion for the field.
✨Tip Number 3
Connect with current employees or alumni from the startup or similar companies on LinkedIn. Ask them about their experiences and any tips they might have for landing a role as an AI Engineer. Personal connections can often lead to referrals.
✨Tip Number 4
Prepare to discuss real-world applications of LLMs in your interviews. Think of specific examples where you've fine-tuned models or integrated them into applications. This practical insight will set you apart from other candidates.
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Large Language Models (LLMs) and relevant machine learning projects. Use specific examples that demonstrate your skills in fine-tuning and deploying LLMs.
Craft a Compelling Cover Letter: Write a cover letter that showcases your passion for AI and your desire to be part of a founding team. Mention how your background aligns with the startup's goals and how you can contribute to their innovative projects.
Showcase Relevant Skills: In your application, emphasise your proficiency with frameworks like Hugging Face Transformers and your programming skills in Python. Highlight any experience with prompt engineering and model evaluation methods.
Research the Company: Familiarise yourself with UpViVA and their mission in the AI space. Understanding their products and the impact of LLMs on their offerings will help you tailor your application and prepare for potential interviews.
How to prepare for a job interview at Job Traffic
✨Showcase Your LLM Expertise
Be prepared to discuss your hands-on experience with large language models. Highlight specific projects where you've fine-tuned or customised LLMs, and be ready to explain the impact of your work on real-world applications.
✨Demonstrate Your Problem-Solving Skills
Think of examples where you've tackled complex challenges in machine learning or NLP. Discuss how you approached these problems, the methodologies you used, and the outcomes. This will show your ability to drive results in a startup environment.
✨Stay Updated on LLM Trends
Research the latest advancements in LLMs and be ready to discuss them during your interview. Mention any open-source developments or benchmarks that excite you, as this demonstrates your passion for the field and commitment to continuous learning.
✨Prepare for Technical Questions
Expect technical questions related to Python programming, ML libraries, and prompt engineering best practices. Brush up on your knowledge of model evaluation methods and be ready to explain how you would optimise performance for accuracy and cost.
