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- Tasks: Join us to develop cutting-edge LLM applications and autonomous agents.
- Company: Be part of an innovative tech firm pushing the boundaries of AI technology.
- Benefits: Enjoy a dynamic work environment with opportunities for growth and collaboration.
- Why this job: Work on impactful projects in conversational AI and shape the future of technology.
- Qualifications: Strong Python skills and deep knowledge of machine learning and AI methodologies required.
- Other info: We're looking for 5 talented individuals to join our team!
The predicted salary is between 43200 - 72000 £ per year.
Innovative Tech firm developing unique LLM applications is hiring a Deep Learning Scientist.
The nature of your work will focus on autonomous agent development & interactions at scale.
Experience Required:
- Python, with solid software design experience.
- Deep knowledge of machine learning, deep learning methodologies & transformers.
- Conversational AI technologies, like natural language understanding/generation, dialog systems, machine translation, and information retrieval.
- Experience in developing information retrieval systems, Fine Tuning for RAG & Direct Preference optimisation.
- Experience in ML Ops environments & platforms.
The more experience you have in adapting LLMs for different domains, the better (although this isn’t a must-have), so someone who has worked in a consulting environment across domains might be relevant.
If this sounds like you and you’d like to discuss further, please get in touch. We are currently retained for 5 hires for our client.
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Deep Learning Scientist – LLM’s, RAG employer: Wyatt Partners
Contact Detail:
Wyatt Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Deep Learning Scientist – LLM’s, RAG
✨Tip Number 1
Make sure to showcase your experience with Python and deep learning methodologies in your conversations. Highlight specific projects where you've implemented transformers or conversational AI technologies, as this will resonate well with our team.
✨Tip Number 2
Familiarize yourself with the latest trends in autonomous agent development and information retrieval systems. Being able to discuss recent advancements or challenges in these areas can set you apart during discussions.
✨Tip Number 3
If you have experience in ML Ops environments, be prepared to share how you've integrated machine learning models into production. This practical knowledge is crucial for the role and will demonstrate your readiness to hit the ground running.
✨Tip Number 4
Consider discussing any consulting experience you have, especially if it involved adapting LLMs for different domains. This can illustrate your versatility and ability to tackle diverse challenges, which is highly valued in our innovative environment.
We think you need these skills to ace Deep Learning Scientist – LLM’s, RAG
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with Python, machine learning, and deep learning methodologies. Include specific projects or roles where you developed conversational AI technologies or worked with LLMs.
Craft a Strong Cover Letter: In your cover letter, emphasize your expertise in autonomous agent development and your familiarity with ML Ops environments. Mention any relevant consulting experience that showcases your ability to adapt LLMs across different domains.
Showcase Relevant Projects: If you have worked on projects involving fine-tuning for RAG or direct preference optimization, be sure to detail these experiences. Use metrics or outcomes to demonstrate the impact of your work.
Highlight Continuous Learning: Mention any recent courses, certifications, or workshops related to deep learning or conversational AI. This shows your commitment to staying updated in a rapidly evolving field.
How to prepare for a job interview at Wyatt Partners
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python and deep learning methodologies in detail. Highlight specific projects where you've implemented transformers or conversational AI technologies, as this will demonstrate your hands-on expertise.
✨Discuss Your Experience with LLMs
If you have experience adapting large language models for different domains, make sure to share those examples. Even if it's not a must-have, showcasing this adaptability can set you apart from other candidates.
✨Prepare for Problem-Solving Questions
Expect to face technical challenges during the interview. Practice explaining your thought process when solving problems related to machine translation, dialog systems, or information retrieval. This will show your analytical skills and ability to think on your feet.
✨Familiarize Yourself with ML Ops
Since experience in ML Ops environments is mentioned, brush up on relevant tools and platforms. Be ready to discuss how you've integrated ML Ops into your previous projects, as this knowledge is crucial for the role.
