

Deloitte
AI Engineer - RAG (Retrieval-Augmented Generation) / Vector Databases and Langchain
β - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer - RAG, focusing on Retrieval-Augmented Generation, Vector Databases, and LangChain in London. Contract duration is 6 months, with a pay rate classified as inside IR35. Key skills include Python, Prompt Engineering, and cloud infrastructure experience.
π - Country
United States
π± - Currency
$ USD
-
π° - Day rate
Unknown
-
ποΈ - Date
April 28, 2026
π - Duration
More than 6 months
-
ποΈ - Location
Hybrid
-
π - Contract
Inside IR35
-
π - Security
Unknown
-
π - Location detailed
City Of London, England, United Kingdom
-
π§ - Skills detailed
#Cloud #Databases #AI (Artificial Intelligence) #Classification #Deployment #Langchain #Azure #Python #Documentation #Microsoft Azure #Scala
Role description
Contract Role: AI Engineer - RAG (Retrieval-Augmented Generation) / Vector Databases and Langchain
Contract Location: London. 2/3 days hybrid
Contract Duration: 6 months
Contract Start Date: Immediate
Contract Classification: Inside IR35
Role Overview
We are supporting a leading financial services organisation in hiring an experienced AI Engineer to support the development and optimisation of advanced GenAI solutions. This role will focus heavily on Retrieval-Augmented Generation (RAG), Vector Databases, Prompt Engineering, and LangChain frameworks to enhance enterprise AI capabilities.
The successful candidate will play a key role in designing, building, and improving scalable AI applications, working closely with technical and business stakeholders to deliver high-impact solutions within a fast-paced environment.
Key Responsibilities
β’ Design, build, and optimise AI solutions using Retrieval-Augmented Generation (RAG) frameworks
β’ Develop and maintain integrations with Vector Databases for efficient retrieval and contextual generation
β’ Work extensively with LangChain to build and enhance LLM-based applications
β’ Apply strong Prompt Engineering techniques to improve model performance and output quality
β’ Develop scalable Python-based solutions to support GenAI initiatives
β’ Collaborate with engineering, architecture, and business teams to translate requirements into technical delivery
β’ Support continuous improvement of AI workflows, performance, and governance standards
β’ Contribute to documentation, testing, and deployment best practices
Required Skills & Experience
β’ Strong Python development experience
β’ Proven experience with Prompt Engineering and LLM optimisation
β’ Hands-on experience with Retrieval-Augmented Generation (RAG)
β’ Strong understanding of Vector Databases and retrieval frameworks
β’ Experience using LangChain in enterprise or production environments
β’ Experience working within cloud-based infrastructure and modern engineering environments
β’ Strong problem-solving and stakeholder management skills
Preferred Skills
β’ Experience with Microsoft Azure
β’ Experience with Agent Development Kit (ADK)
β’ Exposure to enterprise AI governance and production deployment of GenAI solutions
β’ Financial services experience preferred but not essential
Contract Role: AI Engineer - RAG (Retrieval-Augmented Generation) / Vector Databases and Langchain
Contract Location: London. 2/3 days hybrid
Contract Duration: 6 months
Contract Start Date: Immediate
Contract Classification: Inside IR35
Role Overview
We are supporting a leading financial services organisation in hiring an experienced AI Engineer to support the development and optimisation of advanced GenAI solutions. This role will focus heavily on Retrieval-Augmented Generation (RAG), Vector Databases, Prompt Engineering, and LangChain frameworks to enhance enterprise AI capabilities.
The successful candidate will play a key role in designing, building, and improving scalable AI applications, working closely with technical and business stakeholders to deliver high-impact solutions within a fast-paced environment.
Key Responsibilities
β’ Design, build, and optimise AI solutions using Retrieval-Augmented Generation (RAG) frameworks
β’ Develop and maintain integrations with Vector Databases for efficient retrieval and contextual generation
β’ Work extensively with LangChain to build and enhance LLM-based applications
β’ Apply strong Prompt Engineering techniques to improve model performance and output quality
β’ Develop scalable Python-based solutions to support GenAI initiatives
β’ Collaborate with engineering, architecture, and business teams to translate requirements into technical delivery
β’ Support continuous improvement of AI workflows, performance, and governance standards
β’ Contribute to documentation, testing, and deployment best practices
Required Skills & Experience
β’ Strong Python development experience
β’ Proven experience with Prompt Engineering and LLM optimisation
β’ Hands-on experience with Retrieval-Augmented Generation (RAG)
β’ Strong understanding of Vector Databases and retrieval frameworks
β’ Experience using LangChain in enterprise or production environments
β’ Experience working within cloud-based infrastructure and modern engineering environments
β’ Strong problem-solving and stakeholder management skills
Preferred Skills
β’ Experience with Microsoft Azure
β’ Experience with Agent Development Kit (ADK)
β’ Exposure to enterprise AI governance and production deployment of GenAI solutions
β’ Financial services experience preferred but not essential






