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- Tasks: Design and build advanced agent-driven AI systems for real-time feedback.
- Company: Leading AI company in Greater London with a focus on innovation.
- Benefits: Competitive salary, flexible working hours, and opportunities for professional growth.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: Experience in building AI systems and strong software engineering skills required.
- Other info: Collaborative environment with exciting projects and career advancement potential.
The predicted salary is between 60000 - 80000 £ per year.
A leading AI company in Greater London is seeking a Machine Learning Engineer to design and build advanced agent-driven systems. You will work on integrating AI models into production environments with a strong focus on real-time feedback and collaboration with product teams.
Candidates should have significant experience building AI systems beyond basic LLM usage and strong software engineering skills. Familiarity with frameworks like LangChain or LlamaIndex is preferred.
Agentic AI Engineer - Production Multi-Agent Systems employer: Thyme
Contact Detail:
Thyme Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Agentic AI Engineer - Production Multi-Agent Systems
✨Tip Number 1
Network like a pro! Reach out to folks in the AI community, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving agent-driven systems or frameworks like LangChain. This gives potential employers a taste of what you can do beyond just a CV.
✨Tip Number 3
Prepare for interviews by brushing up on real-time feedback integration and collaboration techniques. Be ready to discuss how you've tackled challenges in past projects and how you can bring that experience to their team.
✨Tip Number 4
Don't forget to apply through our website! We make it easy for you to find roles that match your skills and interests. Plus, it shows you're genuinely interested in joining our team!
We think you need these skills to ace Agentic AI Engineer - Production Multi-Agent Systems
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your experience with AI systems and software engineering in your application. We want to see how you've gone beyond just basic LLM usage, so share specific projects or achievements that demonstrate your expertise.
Tailor Your Application: Don’t just send a generic CV and cover letter! Take the time to tailor your application to the role of Agentic AI Engineer. Mention your familiarity with frameworks like LangChain or LlamaIndex, and explain how they relate to the work we do at StudySmarter.
Be Clear and Concise: When writing your application, keep it clear and to the point. We appreciate well-structured applications that are easy to read. Use bullet points where necessary to make your skills and experiences stand out!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re genuinely interested in joining our team!
How to prepare for a job interview at Thyme
✨Know Your AI Models
Make sure you’re well-versed in the AI models you’ve worked with, especially beyond basic LLM usage. Be ready to discuss specific projects where you integrated these models into production environments and how you tackled challenges along the way.
✨Showcase Your Software Engineering Skills
Prepare to demonstrate your software engineering prowess. Bring examples of code or projects that highlight your ability to build robust systems. If you've used frameworks like LangChain or LlamaIndex, be ready to explain how they contributed to your projects.
✨Collaboration is Key
Since the role involves working closely with product teams, think of examples where you successfully collaborated with others. Highlight your communication skills and how you’ve used feedback to improve your AI systems.
✨Real-Time Feedback Focus
Be prepared to discuss how you handle real-time feedback in your projects. Share experiences where you adapted your systems based on user input or performance metrics, showcasing your agility and responsiveness in a fast-paced environment.