Advanced Resource Managers UK
AI Application/Big Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Application/Big Data Engineer on a 6-month contract, paying market rate. Located in London with hybrid work (up to 3 days onsite), it requires 5-10 years of experience in Data/AI Engineering, strong AWS skills, and finance sector experience.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
Unknown
-
🗓️ - Date
May 29, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Hybrid
-
📄 - Contract
Inside IR35
-
🔒 - Security
Unknown
-
📍 - Location detailed
London Area, United Kingdom
-
🧠 - Skills detailed
#Snowflake #Scala #UAT (User Acceptance Testing) #Microservices #AWS (Amazon Web Services) #Data Pipeline #Big Data #Cloud #SharePoint #Observability #REST (Representational State Transfer) #Python #Amplitude #Logging #NLP (Natural Language Processing) #PostgreSQL #SQS (Simple Queue Service) #Data Engineering #RDS (Amazon Relational Database Service) #S3 (Amazon Simple Storage Service) #REST API #AI (Artificial Intelligence) #Documentation #Batch #Data Processing
Role description
AI Application/Big Data Engineer
6-Month contract – Inside IR35 – market rate
London based – hybrid working – up to 3 days a week onsite
Finance sector - must have previous experience
Role Overview
Senior AI / Data Engineer responsible for designing, building, and optimizing AI-driven data pipelines and integrations to enable a QAS-powered response suggestion capability embedded in Salesforce Service Cloud. The role focuses on scalable data processing, LLM integration, and continuous model improvement using production telemetry.
Responsibilities
• Design and implement Salesforce ↔ QAS integration architecture
• Build and optimize data pipelines supporting AI inference and feedback loops
• Develop backend services / APIs enabling response suggestion workflows
• Integrate LLM capabilities (Amazon Bedrock) for response generation and embeddings
• Enable continuous model tuning via:
• telemetry data
• quality scoring
• usage analytics
• Work with structured and unstructured data sources:
• Microsoft Graph (SharePoint / Teams)
• Implement asynchronous processing pipelines (SQS, EventBridge)
• Ensure data reliability, scalability, and performance
• Contribute to:
• design documentation
• runbooks
• technical decision-making
• Support:
• SIT/UAT phases
• production readiness
• hypercare and rollout to additional entities
Required Experience & Skills
Core
• 5–10 years of experience in Data Engineering / AI Engineering
• Strong experience in:
• Python / JVM-based backend development
• REST APIs / microservices
• Experience with cloud-native architectures on AWS
Data & AI
• Hands-on with:
• Amazon Bedrock (or equivalent LLM platforms)
• data pipelines (batch + streaming)
• embeddings / retrieval architectures
• Experience using:
• Snowflake (data platform integration, CDC concepts)
• PostgreSQL (RDS)
AWS Stack
• S3, RDS, SQS, EventBridge
• Containerized workloads (EKS/ECS)
Engineering Practices
• Strong understanding of:
• distributed systems
• performance optimization
• observability (e.g. Langfuse, logging/metrics)
Nice-to-Have
• Experience with:
• Salesforce Service Cloud integrations
• NLP / GenAI applications in customer service
• Exposure to:
• Amplitude or product analytics tools
• Knowledge of regulated environments (banking / capital markets)
Soft Skills
• Ability to work in cross-functional distributed teams
• Strong ownership mindset (design → production)
• Clear communication with business and technical stakeholders
AI Application/Big Data Engineer
6-Month contract – Inside IR35 – market rate
London based – hybrid working – up to 3 days a week onsite
Finance sector - must have previous experience
Role Overview
Senior AI / Data Engineer responsible for designing, building, and optimizing AI-driven data pipelines and integrations to enable a QAS-powered response suggestion capability embedded in Salesforce Service Cloud. The role focuses on scalable data processing, LLM integration, and continuous model improvement using production telemetry.
Responsibilities
• Design and implement Salesforce ↔ QAS integration architecture
• Build and optimize data pipelines supporting AI inference and feedback loops
• Develop backend services / APIs enabling response suggestion workflows
• Integrate LLM capabilities (Amazon Bedrock) for response generation and embeddings
• Enable continuous model tuning via:
• telemetry data
• quality scoring
• usage analytics
• Work with structured and unstructured data sources:
• Microsoft Graph (SharePoint / Teams)
• Implement asynchronous processing pipelines (SQS, EventBridge)
• Ensure data reliability, scalability, and performance
• Contribute to:
• design documentation
• runbooks
• technical decision-making
• Support:
• SIT/UAT phases
• production readiness
• hypercare and rollout to additional entities
Required Experience & Skills
Core
• 5–10 years of experience in Data Engineering / AI Engineering
• Strong experience in:
• Python / JVM-based backend development
• REST APIs / microservices
• Experience with cloud-native architectures on AWS
Data & AI
• Hands-on with:
• Amazon Bedrock (or equivalent LLM platforms)
• data pipelines (batch + streaming)
• embeddings / retrieval architectures
• Experience using:
• Snowflake (data platform integration, CDC concepts)
• PostgreSQL (RDS)
AWS Stack
• S3, RDS, SQS, EventBridge
• Containerized workloads (EKS/ECS)
Engineering Practices
• Strong understanding of:
• distributed systems
• performance optimization
• observability (e.g. Langfuse, logging/metrics)
Nice-to-Have
• Experience with:
• Salesforce Service Cloud integrations
• NLP / GenAI applications in customer service
• Exposure to:
• Amplitude or product analytics tools
• Knowledge of regulated environments (banking / capital markets)
Soft Skills
• Ability to work in cross-functional distributed teams
• Strong ownership mindset (design → production)
• Clear communication with business and technical stakeholders
