> Markdown version of [/jobs/ext/3792978-software-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3792978-software-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software AI Engineer - **Company:** Deloitte - **Location:** London, UK - **Experience:** Experienced - **Salary:** £68,758.0 - **Contract:** Permanent contract - **Skills:** Testing (Software), AI Evaluation, Java (Programming Language), Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Application Integration Architecture, Software Applications, Automation of Tests, Big Data, Cloud Computing, Continuous Integration, Python (Programming Language), Machine Learning, Commercial Software, Regression Testing, Cloud Services, Microsoft Copilot, Software Engineering, SQL Databases, TypeScript, Web Platforms, Data Processing, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Software Security, Generative AI, Agentic-AI, Containerization, Kubernetes, Data Management, Machine Learning Operations, Virtual Agents, Model Context Protocol, Dynatrace, Databricks, Microservices - **Published:** October 17, 2026 - **Apply:** https://www.adzuna.co.uk/jobs/details/5931028957 ## About the Role * 3+ years of commercial software engineering experience, with a strong background in application development and system design. * Strong proficiency in Python, with experience in at least one additional language such as Java, TypeScript or SQL. * Experience developing and integrating APIs, microservices and event-driven applications. * Working knowledge of Generative AI, Large Language Models (LLMs), RAG, prompt engineering and AI agent frameworks. * Experience working with large datasets and data processing technologies such as Apache Spark. * Strong understanding of software testing, CI/CD, application security and operational resilience. * Practical experience with cloud-native technologies, containers and Kubernetes. * Experience working within Agile development environments. * Understanding of secure software development practices and responsible AI principles. Nice to Have * Experience with AWS cloud services. * Hands-on experience with Databricks and MLOps frameworks. * Knowledge of Model Context Protocol (MCP) and AI agent orchestration. * Experience with AI evaluation frameworks, LLM-as-a-judge techniques and automated regression testing. * Familiarity with OpenTelemetry, observability and distributed tracing. * Experience developing AI-powered copilots or enterprise GenAI applications. * Exposure to financial services, banking or other highly regulated environments. * Experience using AI-assisted development tools. ## Description We are looking for experienced Software AI Engineers to join a leading global financial services organisation, working on innovative AI-driven solutions and next-generation digital platforms. This is an exciting opportunity to develop and enhance intelligent AI agents, copilots and machine learning applications within a large-scale enterprise environment. You will work across software engineering, Generative AI, cloud technologies and data platforms, helping to build secure, scalable and production-ready AI solutions., * Design, develop and maintain AI-powered applications, intelligent agents and copilot solutions using Python. * Build and integrate APIs, agent tools and Model Context Protocol (MCP) servers to connect AI applications with enterprise systems. * Develop retrieval-augmented generation (RAG) pipelines, including document ingestion, embeddings, indexing and retrieval. * Build and maintain data processing pipelines using Databricks and Apache Spark. * Support the development, testing and deployment of machine learning models through MLOps pipelines. * Develop user-facing AI interfaces, including streaming interactions and human-in-the-loop workflows. * Implement automated testing, AI model evaluations and CI/CD processes to ensure application quality and reliability. * Troubleshoot technical issues, optimise performance and maintain secure, scalable cloud-native applications. * Collaborate with cross-functional engineering, data science and product teams to deliver high-quality AI solutions.