Machine Learning & AI Engineer (LLMs & Generative AI)

www.findapprenticeship.service.gov.uk - Jobboard; United Kingdom; On-site; GBP 60,000 - 90,000; Full time

Job summary

An AI solutions provider is seeking a Machine Learning & AI Engineer to develop advanced AI solutions. This role involves designing and deploying models, and managing data pipelines, with a focus on LLMs. Candidates should have experience in Python and MLOps tools. The position offers an innovative environment for those passionate about AI technology.

Qualifications

• Proven experience in machine learning model development, especially with LLMs.
• Strong Python skills with experience in PyTorch or TensorFlow.
• Understanding of vector search engines and embeddings.

Responsibilities

• Design, train, and fine-tune LLMs and other deep learning architectures.
• Build and maintain scalable ETL/ELT pipelines for data.
• Deploy AI models in production using MLOps best practices.

Skills

Machine Learning

Natural Language Processing

Python

MLOps

Cloud Platforms

Tools

PyTorch

TensorFlow

Hugging Face Transformers

Docker

Kubernetes

Job description

We are seeking a highly skilled Machine Learning & AI Engineer with expertise in Large Language Models (LLMs), Natural Language Processing (NLP), and cutting-edge AI techniques. The successful candidate will be responsible for designing, developing, and deploying advanced AI solutions, including fine-tuning and optimizing large-scale models, building data pipelines, and integrating AI capabilities into production systems.

You will collaborate closely with cross-functional teams, including data scientists, ML engineers, product managers, and software developers, to deliver scalable, secure, and high-performance AI solutions that meet business objectives.

Key Responsibilities
Model Development & Optimisation
Design, train, and fine-tune LLMs (e.g., GPT, LLaMA, Mistral) and other deep learning architectures for specific use cases.

Apply techniques such as prompt engineering, retrieval-augmented generation (RAG), parameter-efficient fine-tuning (PEFT), and model distillation.

Implement multi-modal AI systems (text, image, speech) where relevant.

Data Engineering & Pipeline Management
Build and maintain scalable ETL/ELT pipelines for structured and unstructured data.

Perform data cleaning, augmentation, tokenisation, and feature engineering for AI workloads.

Manage vector databases (e.g., Pinecone, Weaviate, Milvus) for embedding search.

Deployment & Integration
Deploy AI models into production using MLOps best practices (e.g., MLflow, Kubeflow, Vertex AI, SageMaker).

Integrate LLM capabilities into APIs, chatbots, and business applications.

Ensure high availability, low latency, and horizontal scalability.

Research & Innovation
Stay ahead of developments in AI, ML, and LLM architectures, including transformer optimisations and mixture-of-experts (MoE) models.

Experiment with open-source and proprietary AI frameworks to assess suitability for business problems.

Publish findings, contribute to internal knowledge bases, and present results to stakeholders.

Governance, Ethics & Compliance
Ensure compliance with data privacy regulations (GDPR, EU AI Act, CCPA).

Implement AI safety, bias mitigation, and explainability techniques (XAI).

Work with legal and compliance teams on AI risk assessments and governance frameworks.

Required Skills & Experience
Proven experience in machine learning model development, especially transformer-based LLMs.

Strong Python skills with experience in PyTorch, TensorFlow, or JAX.

Experience with Hugging Face Transformers, LangChain, or similar frameworks.

Understanding of vector search engines and embeddings.

Experience with MLOps tools (Docker, Kubernetes, CI/CD for ML).

Knowledge of NLP techniques: tokenisation, attention mechanisms, text classification, summarisation, NER.

Strong grasp of cloud platforms (AWS, GCP, Azure) and GPU/TPU acceleration.

Familiarity with AI ethics, model interpretability, and gove
Experience with multi-modal AI and agentic workflows.

Prior work in retrieval-augmented generation (RAG) and domain-specific fine-tuning.

Contributions to open-source AI/ML projects.