AI Developer (LLM Specialist), Retrain and Fine-Tume LLMs On Our Datasets

Purple Dot Digital Limited
London
1 year ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Data Science and Innovation Manager

Deep Learning Engineer: Build Scalable AI & Deploy Models

Lead Graphic Designer | AI & Computer Vision | SaaS | up to £100k

Lead Graphic Designer | AI & Computer Vision | SaaS | up to £100k

Java UI & Data Science Developer — Flexible Work

We are seeking an experienced AI Developer with a strong background in Large Language Models (LLMs) to join our AI team. The ideal candidate will have expertise in retraining and fine-tuning LLMs using proprietary datasets to build a conversational chat bot.

Tasks

Key Responsibilities:

  • Model Development:Retrain and fine-tune existing Large Language Models (LLMs) using proprietary datasets to meet specific business requirements.
  • Data Integration:Work with data engineers and data scientists to curate, preprocess, and integrate company-specific data into LLMs.
  • Model Evaluation:Design and execute experiments to evaluate model performance, accuracy, and scalability, using metrics relevant to the business.
  • Optimization:Implement model optimization techniques to improve efficiency, reduce latency, and enhance model scalability.
  • Collaboration:Collaborate with cross-functional teams, including product managers, data scientists, and software engineers, to align AI solutions with business goals.
  • Deployment:Assist in deploying LLMs into production environments, ensuring robust and scalable AI solutions.

Requirements

Qualifications:

  • Experience:3-5 years of experience in AI/ML development, with a focus on working with Large Language Models (e.g., GPT, BERT, Hugging Face, etc.).

  • Education:Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience).

  • Technical Skills:

  • Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch).

  • Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms.

  • Experience in retraining and fine-tuning LLMs using large-scale datasets.

  • Familiarity with cloud platforms like AWS, Google Cloud, or Azure for model training and deployment.

  • Data Skills:Expertise in data preprocessing, augmentation, and management of large datasets for training purposes.

  • Problem-Solving:Strong analytical and problem-solving skills, with the ability to address complex AI challenges.

  • Communication:Excellent communication skills to explain technical concepts to non-technical stakeholders.

  • Version Control:Proficiency with version control tools such as Git.

Required skills

  • Experience in retraining LLMs on various datasets
  • Conversational AI
  • Python
  • Docker

Preferred Skills:

  • Experience with specialized AI domains such as conversational AI, sentiment analysis, or recommendation systems.
  • Knowledge of model interpretability techniques and responsible AI practices.
  • Familiarity with MLOps pipelines for continuous integration and deployment of AI models.
  • Experience with API development and integration for deploying AI services.
  • Prior experience in working with proprietary or sensitive data.

Benefits

What We Offer:

  • Competitive Salary:Based on experience and expertise.
  • Professional Growth:Opportunities for career development, including access to the latest AI research and technologies.
  • Flexible Work Environment:Options for remote work and flexible hours to promote work-life balance.
  • Innovative Culture:Join a forward-thinking team that values creativity, collaboration, and innovation.

Interested candidates are invited to submit their resume, a cover letter, and any relevant project portfolios.

UK Skilled Worker Visa Sponsorship

We do not offer UK Skilled Worker Visa Sponsorship. If you are UK resident then you must have working UK VISA to apply for this job.



We specialize in building marketplace mobile and web apps. We work with a vast range of technology stack.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.