AI​/Big Data Engineer

AI Application/Big Data Engineer

6-Month contract - Inside IR35 - market rate

London based - hybrid working - up to 3 days a week onsite

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.

• Design and implement Salesforce and 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, Event Bridge)
• 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

• 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

• Hands‑on with:
  • Amazon Bedrock (or equivalent LLM platforms)
  • data pipelines (batch + streaming)
  • embeddings / retrieval architectures
• Experience using:
  • Snowflake (data platform integration, CDC concepts)
  • Postgre
    
    SQL (RDS)

• S3, RDS, SQS, Event Bridge
• Containerized workloads (EKS/ECS)

• Strong understanding of:
  • distributed systems
  • performance optimization
  • observability (e.g. Langfuse, logging/metrics)

• 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)

• Ability to work in cross‑functional distributed teams
• Strong ownership mindset (design to production)
• Clear communication with business and technical stakeholders