At a Glance
- Tasks: Develop and deploy cutting-edge AI applications using advanced techniques and tools.
- Company: Join a forward-thinking tech company focused on generative AI innovation.
- Benefits: Enjoy a competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with potential for contract extension and career advancement.
- Why this job: Be at the forefront of AI technology and make a real difference in the industry.
- Qualifications: Strong programming skills in Python and experience with AI/ML frameworks required.
The predicted salary is between 60000 - 80000 € per year.
Role: GEN AI
Type: 1 year Fixed Term Contract (Chance for Extension)
Core AI/ML Foundations:
- Strong foundational knowledge in GenAI, Machine Learning (ML modeling), Data Science, Statistics, and AI fundamentals, including Natural Language Processing (NLP), Neural Networks, and Large Language Models (LLMs).
Generative AI & LLM Expertise:
- Deep working knowledge and hands-on experience with Retrieval-Augmented Generation (RAG) pipelines, including advanced RAG techniques and their detailed implementation.
- Proven ability to build, tune, and deploy LLM-based applications using platforms like Vertex AI, Hugging Face, etc.
- Hands-on experience with agentic framework-based use case implementation.
- Working knowledge of Guardrails and methodologies for assessing the performance and safety of GenAI features.
Programming & Data Engineering:
- Strong programming proficiency in Python is a must, including extensive experience with libraries such as Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Transformers, FastAPI, Seaborn, LangChain, and LlamaIndex.
- Proficiency in integrating generative AI with enterprise applications using APIs, knowledge graphs, and orchestration tools.
- Hands-on experience with various vector databases (e.g., PG Vector, Pinecone, Mongo Atlas, Neo4j) for efficient data storage and retrieval.
- Experience in dealing with large amounts of unstructured data and designing solutions for high-throughput processing.
Deployment & MLOps:
- Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines.
- Experience with Jenkins, GitLab CI, Azure DevOps, ArgoCD for automated builds, testing, and deployments.
AI Engineer in City of London employer: Ubique Systems
As an AI Engineer at our innovative company, you will thrive in a dynamic work culture that prioritises collaboration and creativity. We offer competitive benefits, including professional development opportunities and a supportive environment that encourages continuous learning in the rapidly evolving field of Generative AI. Located in a vibrant tech hub, our team is dedicated to pushing the boundaries of AI technology while ensuring a healthy work-life balance for all employees.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML community on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can land you that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Generative AI and LLMs. We love seeing hands-on experience, so make sure to highlight any cool applications you've built or contributed to.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practicing common AI/ML interview questions and even doing mock interviews with friends or mentors to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always on the lookout for passionate candidates who are eager to dive into the world of AI.
We think you need these skills to ace AI Engineer in City of London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your strong foundational knowledge in GenAI, ML modelling, and all those fancy AI fundamentals. We want to see your hands-on experience with RAG pipelines and LLM-based applications, so don’t hold back!
Tailor Your Application:Customise your application to match the job description. Use keywords from the listing, like Python proficiency and MLOps principles, to show us you’re the perfect fit for the role. It’s all about making that connection!
Be Clear and Concise:Keep your application clear and to the point. We appreciate a well-structured application that gets straight to the good stuff. Avoid fluff and focus on what makes you stand out as an AI Engineer.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Ubique Systems
✨Know Your AI Fundamentals
Make sure you brush up on your core AI and ML knowledge. Be ready to discuss concepts like Natural Language Processing, Neural Networks, and the intricacies of Large Language Models. This will show that you have a solid foundation and can engage in technical discussions.
✨Showcase Your RAG Expertise
Since the role requires hands-on experience with Retrieval-Augmented Generation pipelines, prepare to talk about your past projects. Bring examples of how you've implemented advanced RAG techniques and the impact they had on your applications. Real-world examples will make your experience stand out.
✨Demonstrate Your Programming Skills
Be ready to discuss your proficiency in Python and the libraries mentioned in the job description. You might even be asked to solve a coding problem on the spot, so practice coding challenges using Pandas, NumPy, or PyTorch to keep your skills sharp.
✨Understand MLOps Principles
Familiarise yourself with MLOps practices and tools like Jenkins and Azure DevOps. Be prepared to explain how you've established deployment pipelines in your previous roles. Showing that you can manage the entire lifecycle of machine learning models will give you an edge.
