Machine Learning Engineer II

Zonda
Glasgow
5 months ago
Applications closed

Related Jobs

View all jobs

AI/ML Software Engineer III — GenAI & NLP Pipelines

Data Engineer III - Python, Databricks & AWS

Software Engineer III- Data Engineer, Java/Python

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...]

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...]

Engineering Manager, Machine Learning, Marketplace, Ecommerce, | 35 Million Users | UK Remote O[...]

Machine Learning Engineer (NLP) II

Remote | UK | Full Time

The ML Engineer (Natural Language Processing) II is a mid-level position responsible for Natural Language models-based product development and maintenance. As a mid-level engineer in this role, you will work closely with senior engineers and non-technical business stake holders, contributing to the entire lifecycle of building and maintaining emerging LLM/NLP-based ML products. An important aspect of this role would be product design and execution bearing cost efficiency and speed. The ideal candidate would have skills in software programming, ML, agentic workflows, mathematics, and DevOps. Good communication skills with an ability to demystify ML algorithms and capabilities is a must, as stakeholders in ML products include software engineering teams as well as non-technical business partners.

What You'll Do

Curation, cleaning, and maintenance of datasets for NLP/LLM models. Develop, optimise, and deploy NLP/LLM models. Work with agentic frameworks like LangChain, CrewAI, or AutoGen to develop multi[1]step agents or workflows Work with Product stakeholders to capture and establish requirements for natural language models-based products. Collaborate with Product Managers, software engineering teams, and other departments to design NLP products for inference. Steer consolidation of dataset requirements, acquiring data, annotation, management, and version control for NLP applications. Monitor ML models in production, setting metrics to identify drift, and establish corrective measures for restoring model performance. Identify and implement appropriate tools for monitoring product performance in inference. Ownership of technical documentation related to datasets, model selection, training experiments, and production infrastructure. Continual learning and self-improvement with a focus on latest trends, techniques, and best practices in Machine Learning.

Who You Are

Bachelor's degree in computer science, Engineering, or a related field. 3+ years of experience in Machine Learning, Data Science, or a related field. Proficient in Python and working knowledge of ML libraries PyTorch and scikit-learn. You’ve built and deployed at least one LLM-based or NLP-heavy product in a real setting likely using agentic frameworks like LangGraph, LangChain, AutoGen etc. Strong mathematical, analytical, and problem-solving skills. Experience with retrieval systems, embeddings, and vector DBs like Weaviate or Pinecone. Good understanding of Machine Learning algorithms and models (Language processing models such as GPT, BERT, etc). Experience in designing ML products for inference in cloud. Ability to structure and execute an ML project from start to completion, for both training and inference. Excellent communication and teamwork skills; ability to work in a team. Experience with cloud computing platforms like AWS, Google Cloud, or Azure. Familiarity with containerization and orchestration tools like Docker and Kubernetes. Experience with version control systems like Git.

Nice to have.

Masters in a specific field such as Statistics, Data Science, Machine Learning, or AI. Utilization of Generative AI models. Knowledge of SQL and NoSQL databases including construction of queries, query optimization, and schema design. API development using standard tools such as FastAPI or Flask

Administrative 

The candidate must have the right to work in UK

Why People Love Working Here 

We offer meaningful work and opportunities for career growth Competitive Salary Comprehensive benefit package (Medical, Dental, Vision) Paid vacation and general holidays Education Allowance Employee & Family Assistance Program (EFAP)

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.