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- Tasks: Design and implement cutting-edge NLP models to tackle complex data challenges.
- Company: Join a dynamic AI team in a leading finance company focused on innovation.
- Benefits: Enjoy private healthcare, 28 days leave, enhanced parental leave, and personal development budget.
- Why this job: Be part of a culture that encourages innovation and problem-solving in AI.
- Qualifications: Experience with NLP frameworks, advanced Python skills, and familiarity with cloud AI deployment.
- Other info: Hybrid work model with one day onsite in London.
The predicted salary is between 56000 - 84000 £ per year.
About the job Machine Learning Engineer NLP Specialist
Employment Type: Permanent
- Private healthcare and well-being programme
- 28 days annual leave plus bank holidays
- Enhanced parental leave policies
- Annual performance-based bonus
- Budget for personal development and certifications
About the Role:
Our finance client is seeking a talented Machine Learning Engineer NLP Specialist to join their dynamic AI team. You will play a key role in designing and implementing advanced Natural Language Processing (NLP) models, enabling smarter systems to improve search, information extraction, and classification. If you enjoy solving complex data challenges in an environment where innovation is encouraged, this role is for you.
Key Responsibilities:
- Design and implement state-of-the-art NLP models for tasks such as entity extraction, text summarisation, and semantic understanding.
- Process and analyse diverse datasets, including structured text, tables, and images, to extract meaningful insights.
- Develop indexing and retrieval systems for high-speed, accurate search functionality.
- Explore and implement advanced techniques, such as Retrieval-Augmented Generation (RAG), to enhance AI capabilities.
- Conduct experiments to measure and improve the performance of NLP models.
- Build custom language models tailored to specific industry requirements.
What Were Looking For:
- Proven experience with NLP frameworks such as Hugging Face, spaCy, or TensorFlow.
- Hands-on experience working with Large Language Models (LLMs) and deploying them in production.
- Advanced Python skills, with the ability to develop and optimise machine learning pipelines.
- Experience working with large, unstructured datasets and designing scalable workflows.
- Familiarity with Azure AI, AWS, or Google Cloud for AI deployment.
- Bonus: Knowledge of advanced NLP techniques and RAG.
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Machine Learning Engineer NLP Specialist employer: Eliden
Contact Detail:
Eliden Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Machine Learning Engineer NLP Specialist
✨Tip Number 1
Familiarize yourself with the latest advancements in NLP, especially focusing on frameworks like Hugging Face and spaCy. This will not only enhance your understanding but also demonstrate your commitment to staying updated in a rapidly evolving field.
✨Tip Number 2
Engage with the AI community by participating in forums, attending meetups, or contributing to open-source projects. Networking with professionals in the field can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Showcase your hands-on experience with Large Language Models (LLMs) through personal projects or contributions to existing ones. Having a portfolio that highlights your practical skills can set you apart from other candidates.
✨Tip Number 4
Prepare to discuss specific challenges you've faced while working with unstructured datasets and how you overcame them. Being able to articulate your problem-solving process will demonstrate your expertise and readiness for the role.
We think you need these skills to ace Machine Learning Engineer NLP Specialist
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with NLP frameworks like Hugging Face, spaCy, or TensorFlow. Include specific projects where you've worked with Large Language Models and mention any relevant cloud deployment experience.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and NLP. Discuss how your skills align with the responsibilities of the role, particularly in designing and implementing advanced NLP models. Use specific examples to demonstrate your problem-solving abilities.
Showcase Relevant Projects: If you have worked on projects involving entity extraction, text summarization, or semantic understanding, be sure to include these in your application. Highlight the impact of your work and any innovative techniques you employed.
Proofread Your Application: Before submitting, carefully proofread your application materials. Check for any grammatical errors or typos, as attention to detail is crucial in the tech industry. A polished application reflects your professionalism.
How to prepare for a job interview at Eliden
✨Showcase Your NLP Expertise
Be prepared to discuss your experience with NLP frameworks like Hugging Face, spaCy, or TensorFlow. Highlight specific projects where you designed and implemented NLP models, focusing on the challenges you faced and how you overcame them.
✨Demonstrate Your Problem-Solving Skills
Expect questions that assess your ability to solve complex data challenges. Prepare examples of how you've processed diverse datasets and extracted meaningful insights, particularly in high-pressure situations.
✨Discuss Your Experience with Large Language Models
Since the role requires hands-on experience with LLMs, be ready to explain how you've deployed these models in production. Discuss any performance improvements you achieved and the techniques you used.
✨Familiarize Yourself with Cloud Deployment
As familiarity with Azure AI, AWS, or Google Cloud is important, brush up on your knowledge of these platforms. Be ready to discuss how you've utilized cloud services for AI deployment in your previous roles.
