AI Engineer - Data specialist

Experteer Overview In this role you will design and deliver enterprise-scale AI solutions, combining Generative AI, Agentic AI and RAG to drive measurable business value. You will collaborate with cross-functional teams to deploy production-ready AI capabilities and integrate with data platforms and cloud services. The position emphasizes practical implementation, governance and observability to scale AI across the organization. This is an impact-driven opportunity to shape enterprise AI capabilities in a hybrid London setting. Pay / Benefits

• 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 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 Tasks 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 Key requirements
  
  tools 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 Tasks 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 aK
_ 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 Key requirements