Airswift
Data Scientist / AI Engineer
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Data Scientist / Senior AI Engineer
Duration
12 Months (with potential extension)
Workload
Full Time (40 hours per week)
Setup
Day rate contract inside IR35
Location
Canary Wharf, London (3 days onsite, 2 days remote)
Rate range
£520 - £580 per day
About the Role
Our client is seeking an experienced Data Scientist / AI Engineer to design, build, and deploy production-grade AI solutions that deliver measurable business value across the enterprise.
Key Responsibilities
- Develop and maintain scalable AI-powered applications using modern software engineering best practices.
- Build reusable AI platforms including conversational AI, intelligent search, unstructured data processing, and GenAI-driven business solutions.
- Integrate LLMs, vector databases, RAG architectures, AI agents, embeddings, and orchestration frameworks into production environments.
- Design and implement cloud-native services, APIs, microservices, CI/CD pipelines, testing frameworks, and monitoring solutions.
- Collaborate with business stakeholders, engineers, product teams, and data scientists to rapidly deliver AI solutions.
- Drive automation across data ingestion, preprocessing, model integration, deployment, and evaluation workflows.
- Contribute to AI engineering standards, documentation, and reusable tooling.
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Must-Have Skills
- 5+ years' software engineering experience with production system delivery.
- Strong Python development skills (Java or similar languages also considered).
- Experience building APIs, microservices, and cloud-based applications (AWS, Azure, or GCP).
- Hands-on experience with Generative AI technologies including LLMs, RAG, embeddings, AI agents, and vector databases.
- Strong understanding of DevOps, CI/CD, testing, observability, and scalable architecture.
- Experience deploying reliable, production-grade AI applications.
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Nice-to-Have
- LangChain, Haystack, Hugging Face, Weaviate, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
- Experience with chatbots, enterprise search, or GenAI user interfaces.
- Exposure to unstructured data processing (documents, conversations, images).
- Background in platform engineering, developer tooling, or embedded delivery teams.
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