AI Engineer - Data specialist

We're looking for a Senior AI Engineer – Data & AI Practice to join our team in London, UK, in a hybrid working mode.

In this role, you will design and develop enterprise-scale AI applications leveraging Generative AI, Agentic AI and Retrieval-Augmented Generation (RAG) patterns. You will work on multi-agent orchestration, build reusable frameworks and deploy production-ready solutions that integrate advanced language models into business environments.

This position requires a hands-on engineer who can combine technical expertise in AI platforms, distributed systems and data pipelines with effective collaboration skills. If you have a passion for deploying next-generation AI systems that deliver measurable business value, this is an opportunity to make an impact on innovative, enterprise-level AI capabilities.

Design, build and deploy Generative AI and Agentic AI solutions from prototype to production Develop and optimize RAG pipelines including embeddings, hybrid search, prompt engineering and evaluation frameworks Implement AI agents using frameworks such as LangChain, LangGraph and AutoGen, integrating tools and enterprise workflows Apply modern AI engineering practices, ensuring reproducibility and production readiness in dynamic environments Integrate solutions with enterprise data platforms and cloud services, focusing on scalability and governance standards Leverage tools like Databricks, MLflow and Azure OpenAI for experimentation and deployment Apply DevOps best practices across CI/CD workflows, containerization and automated testing for robust delivery Design and maintain observability and monitoring solutions for AI systems using tools such as Langfuse or Arize Partner with stakeholders to align technical execution with business outcomes and provide technical guidance during architecture discussions Support team knowledge sharing and mentor engineers on AI best practices and delivery standards Bachelor’s or Master’s degree in Computer Science, Engineering or related field; PhD is a plus Proven hands-on experience with Generative AI frameworks, LLMs and agentic architectures Strong practical knowledge of Databricks ecosystem including Delta Lake, Delta Live Tables and governance features Proficiency in Python and working familiarity with SQL or Scala Experience implementing RAG architectures and streaming solutions for AI pipelines Deployment expertise on Azure or multi-cloud environments and familiarity with containerization tools such as Docker Knowledge of AI observability and evaluation solutions for monitoring and performance tuning Strong understanding of MLOps, CI/CD practices and infrastructure automation in AI engineering contexts Demonstrated ability to lead small teams and communicate effectively across technical and non-technical stakeholder groups Experience managing end-to-end delivery from experimentation through production deployment in enterprise contexts Familiarity with vector databases such as Pinecone, Weaviate or Milvus Knowledge of AI governance protocols including safety guardrails and injection-prevention techniques Background working with event-driven architectures or distributed systems Experience fine-tuning or training foundational models and applying advanced prompt engineering techniques