> Markdown version of [/jobs/ext/3042864-staff-software-engineer-ai](https://www.wearedevelopers.com/jobs/ext/3042864-staff-software-engineer-ai). 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).

---

# Staff Software Engineer-AI

- **Company:** Moody's
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Frameworks, Automation of Tests, Microsoft Azure, Cloud Computing, Code Review, Software Debugging, Programming Tools, Distributed Systems, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, Redis, Software Engineering, Software Technical Review, TypeScript, Management of Software Versions, Google Cloud, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Prompt Engineering, Backend, Event Driven Architecture, Build Management, Kubernetes, Information Technology, Deployment Automation, Production Code, Data Pipelines, Serverless Computing, Docker, Golang, Programming Languages
- **Published:** September 24, 2026
- **Apply:** https://www.totaljobs.com/job/staff-software-engineer/moody-s-job108027987

## About the Role

* 8+ years of experience in software engineering, with deep hands-on experience designing, coding, testing, and operating scalable, resilient, production-grade backend systems and cloud-native services
 * Expert-level coding capability in modern programming languages such as Python, TypeScript, Go, or similar, with the ability to personally contribute high-quality production code while guiding technical direction
 * Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques
 * Proven ability to take complex AI solutions from prototype to production, making practical engineering trade-offs across performance, scalability, reliability, security, maintainability, and cost
 * Expert knowledge of cloud platforms such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure, with strong experience using Docker, Kubernetes, Elastic Container Service, or equivalent technologies in production environments
 * Strong experience designing and implementing application programming interfaces, distributed systems, event-driven architectures, data pipelines, PostgreSQL, MongoDB, Redis, vector databases, observability, and automated deployment pipelines
 * Demonstrated ability to influence technical direction while remaining close to the codebase, mentoring engineers through design reviews, code reviews, pairing, debugging, and hands-on problem solving
 * Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency. Strong experience using AI tools to lead innovation initiatives. Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization, * Bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience

## Description

ResponsibilitiesDesign, code, and lead delivery of scalable AI platforms and intelligent applications that bring emerging AI capabilities into production

 * Act as a hands-on technical leader, spending significant time designing, coding, reviewing, debugging, and improving production systems that support AI-powered products and services
 * Design and build scalable backend services, application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale
 * Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration
 * Make key technical decisions while remaining accountable for practical implementation quality, including code maintainability, system performance, reliability, security, scalability, and cost efficiency
 * Establish engineering best practices through hands-on contribution, code reviews, technical design reviews, automated testing, observability, monitoring, and operational excellence
 * Champion machine learning operations practices including model lifecycle management, prompt versioning, automated evaluation, deployment pipelines, monitoring, and continuous improvement
 * Partner with product managers, data scientists, machine learning engineers, engineering leaders, and business stakeholders to translate strategic priorities into robust, buildable technical solutions
 * Build reusable frameworks, libraries, developer tooling, and platform components that accelerate AI development across multiple teams without creating unnecessary abstraction or complexity
 * Evaluate emerging AI technologies through practical prototypes, proof-of-concept builds, and production-readiness assessments, then guide teams on implementation patterns and trade-offs
 * Mentor engineers through practical technical coaching, pairing, code reviews, design feedback, documentation, and example-setting as a senior individual contributor


## Related Videos

- [Reducing LLM Calls with Vector Search Patterns - Raphael De Lio (Redis)](https://www.wearedevelopers.com/videos/1714-reducing-llm-calls-with-vector-search-patterns-raphael-de-lio-redis)
- [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [Developer Experience in the Age of AI](https://www.wearedevelopers.com/videos/1118-developer-experience-in-the-age-of-ai)
- [Accelerating Authentication Architecture: Taking Passwordless to the Next Level](https://www.wearedevelopers.com/videos/733-accelerating-authentication-architecture-taking-passwordless-to-the-next-level)
- [Engineering Mindset in the Age of AI - Gunnar Grosch, AWS](https://www.wearedevelopers.com/videos/1735-engineering-mindset-in-the-age-of-ai-gunnar-grosch-aws)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)