Staff Software Engineer-AI

Moody's Corporation; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

Moody's Corporation seeks a senior software engineer to design and deliver scalable AI platforms and backend systems in a production environment. You will personally contribute high-quality code while guiding architectural direction for AI-powered products and services.

You will build and optimize scalable backend services, data pipelines, and inference workflows, leveraging LLMs, retrieval-augmented generation, and orchestration to drive business impact at enterprise scale.

Qualifications

• 8+ years in software engineering with backend and cloud-native systems.
• Expert coding in Python, TypeScript, Go, with production code.
• Hands-on enterprise AI apps using LLMs, agents, and RAG.
• Experience moving AI solutions from prototype to production.
• Strong cloud experience (AWS/GCP/Azure) and Docker/Kubernetes in production.
• Designing APIs, distributed systems, data pipelines, and observability.

Responsibilities

• Design, code, and deliver scalable AI platforms and intelligent apps.
• Lead hands-on engineering while guiding technical direction.
• Build scalable backend services, data pipelines, and inference workflows.
• Implement LLM-based applications with retrieval and orchestration.
• Establish best practices for testing, monitoring, and reliability.
• Mentor engineers and contribute to architecture reviews.

Skills

Backend engineering

Python

Go

TypeScript

Cloud platforms

Docker/Kubernetes

AI applications

LLMs & AI agents

Data pipelines

PostgreSQL

Education

Bachelor's degree or higher in Computer Science

Tools

Docker

Kubernetes

Elastic Container Service

AWS

GCP

Azure

Vector databases

Job description

• 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

Skills And Competencies

• 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

Education

• Bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience

Responsibilities

Design, 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

About The Team

Our Digital Content and Innovation (DC&I) team is responsible for building next-generation internal and external products powered by cutting-edge artificial intelligence technologies, including large language models, AI agents, machine learning, and natural language processing. The group brings together engineers, data scientists, and AI specialists to solve complex challenges and turn breakthrough ideas into practical products that enhance productivity, unlock insights, and create measurable business impact. Collaboration is central to how the team works. Engineers contribute across the full AI lifecycle, from experimentation and prototyping through to large-scale production deployment, while helping shape reusable platforms, frameworks, and responsible AI practices. By joining the team, you will work on some of the most exciting challenges in applied AI, contribute directly to production code and architecture, and be part of a culture that values curiosity, innovation, knowledge sharing, and continuous growth.