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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Gen AI Engineer - **Company:** Capgemini - **Location:** London, UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Automated Storage and Retrieval Systems, Audit Trail, Microsoft Azure, Batch Processing, BigQuery, Cloud Computing, Continuous Integration, Information Engineering, Data Governance, Data Systems, Distributed Systems, Fraud Prevention and Detection, Graph Database, Python (Programming Language), Standard Sql, Search Technologies, Transaction Data, Unstructured Data, Workflow Management Systems, Chatbots, Azure Data Factory, Retrieval-Augmented Generation, Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Generative AI, Git, Microsoft Fabric, Data Lakes, AI Platforms, Pyspark, Kubernetes, Data Lineage, AWS Data Analytics, Apache Kafka, Machine Learning Operations, Restful APIs, Terraform, Stream Processing, Data Pipelines, Docker, Databricks, Microservices - **Published:** June 14, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=caf6ea19e04f6cea ## About the Role Do you have experience in Terraform?, Do you have a Master's degree?, Technical Skills 7+ years in data engineering / AI engineering Strong Python and SQL expertise Hands-on experience with: LLMs and GenAI frameworks (LangChain, LlamaIndex) Vector databases & embeddings Spark / Databricks / distributed computing Experience in REST APIs and microservices architecture Cloud & Tools Cloud: Azure (preferred), AWS, or GCP Data platforms: Snowflake, Databricks, Delta Lake DevOps: Git, CI/CD tools, Terraform (optional) Containerization: Docker, Kubernetes Financial Services Experience Proven experience in: Banking / Capital Markets / Insurance / FinTech Knowledge of: Market data, trade lifecycle, risk systems, or regulatory reporting Exposure to: KYC/AML, compliance workflows, fraud systems (highly desirable) Preferred Qualifications Experience with Azure OpenAI / Copilot / Microsoft Fabric Knowledge of knowledge graphs and semantic search Exposure to stream processing (Kafka, Event Hub) Certifications: Azure Data Engineer / AI Engineer AWS Data / ML certifications Experience leading teams or mentoring engineers Soft Skills Strong problem-solving and analytical skills Ability to translate business requirements into technical solutions Excellent communication and stakeholder management Experience working in agile environments Nice-to-Have Use Case Experience Intelligent document processing (IDP) GenAI for financial research summarization ## Description We are seeking a Senior Generative AI Data Engineer with strong experience in financial services (banking, capital markets, insurance, or fintech) to design, build, and scale GenAI-powered data solutions. The role combines modern data engineering, ML/LLM integration, and domain-specific financial knowledge to deliver enterprise-grade AI platforms. You will lead the development of data pipelines, vector databases, retrieval systems, and LLM-driven applications to solve use cases such as risk analytics, fraud detection, regulatory compliance, document intelligence, and customer insights Hybrid working: The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time., 1. GenAI & LLM Engineering Design and implement end-to-end GenAI pipelines using LLMs (OpenAI, Azure OpenAI, Anthropic, etc.) Build RAG (Retrieval-Augmented Generation) architectures with vector databases (e.g., Pinecone, FAISS, Azure AI Search) Fine-tune, evaluate, and optimize LLM outputs (prompt engineering, embeddings, guardrails) Develop AI solutions for: Financial document processing (contracts, KYC, trade docs) Regulatory compliance automation Conversational AI/chatbots for banking use cases 2. Data Engineering & Platform Development Build scalable data pipelines using tools like Spark, PySpark, Databricks, Snowflake, or BigQuery Design data lakes/lakehouse architectures on AWS/Azure/GCP Integrate structured and unstructured data (market feeds, transaction data, PDFs, emails) Implement real-time and batch processing frameworks 3. Cloud & MLOps Deploy and manage GenAI solutions on cloud platforms (Azure preferred for financial services) Implement CI/CD pipelines, monitoring, and governance for AI systems Use frameworks such as: MLflow LangChain / LlamaIndex Kubernetes / Docker Ensure scalability, reliability, and security in production environments 4. Financial Services Domain Integration Work with business stakeholders to build solutions aligned with: Risk & compliance (Basel, AML, KYC) Trading & portfolio analytics Fraud detection & anomaly detection Ensure adherence to regulatory and data privacy standards (GDPR, FCA, etc.) 5. 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