AI Developer
| Dyddiad hysbysebu: | 02 Mawrth 2026 |
|---|---|
| Cyflog: | £34,000 i £42,000 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 01 Ebrill 2026 |
| Lleoliad: | Reading |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 1 diwrnod yr wythnos |
| Cwmni: | Diagonal Matrix Ltd |
| Math o swydd: | Parhaol |
| Cyfeirnod swydd: |
Crynodeb
Job Title: AI Developer / AI Engineer (LLM, RAG & Agentic AI)
We are seeking a highly skilled and innovative AI Developer to design, develop, and deploy advanced AI-powered applications using modern LLM, RAG, and Agentic AI architectures. The ideal candidate will have strong hands-on experience in Python-based AI development and practical exposure to frameworks such as LangChain, LangGraph, and OpenAI ecosystems.
You will play a key role in building scalable AI solutions, implementing Retrieval-Augmented Generation (RAG) systems, developing AI agents, and integrating embeddings with vector databases.
Key Responsibilities
AI & LLM Development
Design and implement AI applications using Large Language Models (LLMs).
Integrate OpenAI APIs and other foundation models into production systems.
Develop and optimize prompt engineering strategies for accuracy and performance.
Build and deploy scalable AI services and APIs.
RAG & Knowledge Systems
Design and implement Retrieval-Augmented Generation (RAG) pipelines.
Develop ingestion pipelines for document processing and embedding generation.
Implement semantic search using vector databases.
Optimize chunking strategies, embeddings, and retrieval logic.
Agentic AI & Orchestration
Build intelligent AI Agents using LangChain and LangGraph.
Implement state management, multi-step workflows, and tool integrations.
Design agentic workflows for task automation and decision-making.
Work with specification-driven development approaches for AI systems.
Embeddings & Vector Databases
Generate embeddings using OpenAI or other embedding models.
Implement and manage vector databases (e.g., FAISS, Chroma, Pinecone, etc.).
Optimize similarity search performance and retrieval strategies.
AI Architecture & Integration
Develop AI-driven microservices using Python.
Integrate AI Foundry or enterprise AI platforms into enterprise solutions.
Ensure scalable, secure, and maintainable AI architectures.
Implement monitoring, evaluation, and observability for AI models.
Engineering Best Practices
Follow specification-driven development methodologies.
Write clean, modular, and testable Python code.
Conduct code reviews and maintain high engineering standards.
Collaborate with cross-functional teams (Product, DevOps, Data).
Required Technical Skills (High-Level)
Python (Advanced proficiency)
LangChain
LangGraph
AI Agent Development
Embeddings & Vector Databases
OpenAI API Integration
Large Language Models (LLMs)
AI Foundry Platforms
Prompt Engineering
Specification Driven Development
Retrieval-Augmented Generation (RAG)
Additional Preferred Skills:
Experience with FAISS / Chroma / Pinecone / Weaviate
Experience building REST APIs (FastAPI / Flask)
Understanding of Transformer architecture
Experience with cloud platforms (Azure / AWS / GCP)
Knowledge of CI/CD for AI systems
Experience with evaluation frameworks for LLMs
Exposure to fine-tuning or model optimization techniques
Soft Skills:
Strong analytical and problem-solving ability
Ability to translate business requirements into AI solutions
Excellent communication skills
Self-driven and innovative mindset
Ability to work in fast-paced environments
We are seeking a highly skilled and innovative AI Developer to design, develop, and deploy advanced AI-powered applications using modern LLM, RAG, and Agentic AI architectures. The ideal candidate will have strong hands-on experience in Python-based AI development and practical exposure to frameworks such as LangChain, LangGraph, and OpenAI ecosystems.
You will play a key role in building scalable AI solutions, implementing Retrieval-Augmented Generation (RAG) systems, developing AI agents, and integrating embeddings with vector databases.
Key Responsibilities
AI & LLM Development
Design and implement AI applications using Large Language Models (LLMs).
Integrate OpenAI APIs and other foundation models into production systems.
Develop and optimize prompt engineering strategies for accuracy and performance.
Build and deploy scalable AI services and APIs.
RAG & Knowledge Systems
Design and implement Retrieval-Augmented Generation (RAG) pipelines.
Develop ingestion pipelines for document processing and embedding generation.
Implement semantic search using vector databases.
Optimize chunking strategies, embeddings, and retrieval logic.
Agentic AI & Orchestration
Build intelligent AI Agents using LangChain and LangGraph.
Implement state management, multi-step workflows, and tool integrations.
Design agentic workflows for task automation and decision-making.
Work with specification-driven development approaches for AI systems.
Embeddings & Vector Databases
Generate embeddings using OpenAI or other embedding models.
Implement and manage vector databases (e.g., FAISS, Chroma, Pinecone, etc.).
Optimize similarity search performance and retrieval strategies.
AI Architecture & Integration
Develop AI-driven microservices using Python.
Integrate AI Foundry or enterprise AI platforms into enterprise solutions.
Ensure scalable, secure, and maintainable AI architectures.
Implement monitoring, evaluation, and observability for AI models.
Engineering Best Practices
Follow specification-driven development methodologies.
Write clean, modular, and testable Python code.
Conduct code reviews and maintain high engineering standards.
Collaborate with cross-functional teams (Product, DevOps, Data).
Required Technical Skills (High-Level)
Python (Advanced proficiency)
LangChain
LangGraph
AI Agent Development
Embeddings & Vector Databases
OpenAI API Integration
Large Language Models (LLMs)
AI Foundry Platforms
Prompt Engineering
Specification Driven Development
Retrieval-Augmented Generation (RAG)
Additional Preferred Skills:
Experience with FAISS / Chroma / Pinecone / Weaviate
Experience building REST APIs (FastAPI / Flask)
Understanding of Transformer architecture
Experience with cloud platforms (Azure / AWS / GCP)
Knowledge of CI/CD for AI systems
Experience with evaluation frameworks for LLMs
Exposure to fine-tuning or model optimization techniques
Soft Skills:
Strong analytical and problem-solving ability
Ability to translate business requirements into AI solutions
Excellent communication skills
Self-driven and innovative mindset
Ability to work in fast-paced environments