At a Glance
- Tasks: Develop and deploy cutting-edge GenAI models and applications.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Dynamic work environment with potential for contract extension and career advancement.
- Why this job: Be part of a revolutionary field and shape the future of AI technology.
- Qualifications: Strong programming skills in Python and experience with LLMs and MLOps.
The predicted salary is between 60000 - 80000 € per year.
Type: 1 year Fixed Term Contract (Chance for Extension)
Location: London / Dublin / Belfast
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: Extensive hands-on experience with leading LLMs such as Google Gemini, OpenAI models, Anthropic Claude, Mistral, Llama, and various other open-source LLMs.
Critical: 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. Expertise in developing robust prompt engineering strategies, prompt tuning, and creating reusable prompt templates. 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 Proficiency: 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: Critical: Hands-on experience deploying GenAI-based models to production environments. Strong understanding and practical experience with MLOps principles, model evaluation, and establishing robust deployment pipelines. Strong expertise in CI/CD principles and tools (e.g., Jenkins, GitLab CI, Azure DevOps, ArgoCD) for automated builds, testing, and deployments.
Gen AI Engineer employer: Ubique Systems
As a Gen AI Engineer at our company, you will thrive in a dynamic and innovative environment that champions creativity and collaboration. With a strong focus on employee growth, we offer extensive training opportunities and the chance to work with cutting-edge technologies in vibrant cities like London, Dublin, and Belfast. Our inclusive work culture fosters teamwork and encourages the sharing of ideas, making it an excellent place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Gen AI Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and ML community on LinkedIn or at local meetups. 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 LLMs and GenAI. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Practice common interview questions related to AI/ML and be ready to discuss your hands-on experience with tools like TensorFlow and Hugging Face.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you a better chance of landing that dream role.
We think you need these skills to ace Gen AI Engineer
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with GenAI, ML modelling, and all those fancy libraries like TensorFlow and PyTorch. We want to see how your skills match up with what we're looking for!
Tailor Your Application:Don’t just send a generic CV! Tailor your application to reflect the specific requirements in the job description. Mention your hands-on experience with LLMs and RAG pipelines to catch our eye.
Be Clear and Concise:Keep your application clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications without wading through unnecessary fluff.
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’s super easy!
How to prepare for a job interview at Ubique Systems
✨Know Your GenAI Inside Out
Make sure you brush up on your foundational knowledge in Generative AI, Machine Learning, and Natural Language Processing. Be ready to discuss specific models like Google Gemini or OpenAI's offerings, and share your hands-on experiences with them.
✨Showcase Your RAG Expertise
Since the role requires deep knowledge of Retrieval-Augmented Generation pipelines, prepare to explain advanced techniques you've implemented. Bring examples of how you've built, tuned, and deployed LLM-based applications, especially using platforms like Vertex AI or Hugging Face.
✨Demonstrate Your Programming Skills
Be ready to talk about your Python proficiency and the libraries you've used. Highlight any projects where you've worked with Pandas, NumPy, or TensorFlow, and be prepared to discuss how you integrated generative AI with enterprise applications.
✨Understand MLOps and Deployment
Familiarise yourself with MLOps principles and CI/CD tools like Jenkins or Azure DevOps. Be prepared to discuss your experience in deploying GenAI models to production and how you've established robust deployment pipelines.
