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 *