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# AI Engineer

- **Company:** Version 1 Solutions Limited
- **Location:** Edinburgh, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Software Applications, Cloud Computing, Code Review, DevOps, Python (Programming Language), Machine Learning, Cloud Services, Tensorflow, Search Technologies, SQL Databases, Enterprise Software Applications, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Snowflake, Model Validation, Llamaindex, Generative AI, Agentic-AI, Containerization, AI Platforms, Scikit Learn, Kubernetes, HuggingFace, Machine Learning Operations, Restful APIs, Prompt Templates, Semantic Kernel, Data Pipelines, Docker, Unsupervised Learning, Databricks
- **Published:** November 8, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/ai-engineer/55400429

## About the Role

Strong proficiency in:Python
SQL

AI & Machine Learning
Experience with:Scikit-learn
PyTorch and/or TensorFlow
Hugging Face
Main AI services from leading cloud service and model providers
Supervised and unsupervised learning
Model evaluation
Feature engineering
Statistical concepts

Generative AI
Practical experience building:RAG systems
AI agents
LLM-powered applications
Vector search solutions

Familiarity with:LangChain
LlamaIndex
Semantic Kernel
LangGraph

Cloud & Infrastructure
Experience with AWS.
Docker and containerization.
Kubernetes (preferred).
CI/CD pipelines and DevOps practices.

Professional Experience
3+ years in ML engineering or data science, with demonstrable experience deploying models to production and building Gen AI applications including RAG systems, LLM integration, or agentic workflows
Demonstrated experience delivering AI solutions into production environments.

## Description

AI Application Development
Design, build, and maintain AI-powered applications and services.
Develop and deploy Machine Learning and Generative AI solutions.
Build Retrieval-Augmented Generation (RAG) systems and AI agents.
Integrate foundation models and APIs into enterprise applications.
Create reusable AI components and frameworks.

Machine Learning Engineering
Train, fine-tune, evaluate, and deploy ML models.
Develop feature engineering and model evaluation pipelines.
Implement model monitoring and performance tracking.
Optimize model inference performance and cost efficiency.

Generative AI Engineering
Develop LLM-based applications and workflows.
Build prompt templates, agent frameworks, and orchestration pipelines.
Implement vector search and knowledge retrieval systems.
Design evaluation frameworks for AI quality, safety, and reliability.
Improve hallucination mitigation and response accuracy.

MLOps & Deployment
Design and automate ML model training, validation, and retraining pipelines.
Version and manage datasets, features, and model artefacts using tools such as MLflow or similar.
Deploy and serve ML models via REST APIs or batch inference at scale on Databricks, Snowflake or similar.
Monitor model performance in production, detecting drift and degradation.
Manage feature stores and ensure data pipeline reliability for ML workloads.
Implement experiment tracking and reproducibility across the model lifecycle.

Collaboration
Work closely with Solution Architects, Product Managers, and Engineering Teams.
Participate in code reviews and technical design discussions.
Support testing, deployment, and ongoing optimisation activities.