Data Scientist / AI Engineer

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 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. 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. 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.