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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