Staff Software Engineer - AI

Hackajob Ltd; City of Westminster; On-site; GBP 90,000 - 110,000; Full time

Job summary

Moodys Corporation’s Innovation team is hiring a senior AI Platform Engineer to design, code, and drive scalable AI-powered platforms and intelligent applications in production. You will lead hands-on development, collaborate with data scientists and engineering leaders, and shape reusable AI frameworks and tooling for enterprise-scale deployments.

The role emphasizes building end-to-end AI pipelines, managing performance and security, and mentoring engineers while advancing responsible AI

Qualifications

• 8 years of software engineering experience with backend systems and cloud-native services.
• Expert coding in Python, TypeScript, Java, Go, delivering production code.
• Hands-on experience building enterprise AI apps using LLMs, agents, RAG, prompts, orchestration, eval.
• Ability to move AI solutions from prototype to production with trade-offs on performance, scalability, reliability, security, maintainability, and cost.
• Expert knowledge of AWS, GCP, or Azure with Docker and Kubernetes in production.
• Experience designing APIs, distributed systems, data pipelines, PostgreSQL, MongoDB, Redis, and observability.
• Mentor engineers through design/code reviews, pairing, debugging, and problem solving.
• Deep expertise in AI with track record of production-ready AI solutions for transformation and scalability.
• Commitment to responsible AI practices including governance, monitoring, and continuous improvement.
• Bachelor or higher in CS/Software Eng/AI or equivalent practical experience.

Responsibilities

• Design, code, and lead delivery of scalable AI platforms and intelligent apps.
• Hands-on technical leader, designing, coding, reviewing, debugging, and improving production AI systems.
• Build scalable backend services, APIs, data pipelines, and inference pipelines for real-time/batch workloads.
• Implement LLM apps using retrieval‑augmented generation, prompt orchestration, evaluation, and tool integration.
• Make key technical decisions with focus on maintainability, performance, reliability, security, and cost.
• Establish engineering best practices via code reviews, design reviews, testing, observability, and operations.
• Champion ML Ops including model lifecycle, prompt versioning, deployments, monitoring, and improvements.
• Partner with product managers, data scientists, ML engineers, and stakeholders to translate priorities into robust solutions.
• Build reusable frameworks, libraries, tooling, and platform components for AI across teams.
• Evaluate emerging AI tech through prototypes and POCs, guiding patterns and trade-offs.
• Mentor engineers through coaching, pairing, code reviews, documentation, and example-setting.

Skills

Software Engineering

Backend Design

Production Code

Cloud-Native

Python

TypeScript

Java

Go

AI Systems

LLM Applications

MLOps

System Architecture

Education

Bachelor's degree in CS/Software Engineering/AI or equivalent

Tools

Docker

Kubernetes

PostgreSQL

MongoDB

Redis

Azure

AWS

GCP

Job description

Salary: £100,000 - 100,000 per year

Requirements:

• We require 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.
• We require expert-level coding capability in modern programming languages such as Python, TypeScript, Java, Go, or similar, with the ability to personally contribute high-quality production code while guiding technical direction.
• We require 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.
• We require proven ability to take complex AI solutions from prototype to production, making practical engineering trade‑offs across performance, scalability, reliability, security, maintainability, and cost.
• We require 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.
• We require 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.
• We require 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.
• We require deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation, platform scalability, and operational efficiency.
• We require demonstrated commitment to responsible AI practices, including AI risk awareness, ethical use, governance, evaluation, monitoring, and continuous improvement of AI‑enabled products and services.
• We require a bachelors degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience.

Responsibilities:

• We design, code, and lead delivery of scalable AI platforms and intelligent applications that bring emerging AI capabilities into production.
• We act as hands‑on technical leaders, spending significant time designing, coding, reviewing, debugging, and improving production systems that support AI‑powered products and services.
• We 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.
• We implement advanced large language model applications using retrieval‑augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration.
• We make key technical decisions while remaining accountable for practical implementation quality, including code maintainability, system performance, reliability, security, scalability, and cost efficiency.
• We establish engineering best practices through hands‑on contribution, code reviews, technical design reviews, automated testing, observability, monitoring, and operational excellence.
• We champion machine learning operations practices including model lifecycle management, prompt versioning, automated evaluation, deployment pipelines, monitoring, and continuous improvement.
• We partner with product managers, data scientists, machine learning engineers, engineering leaders, and business stakeholders to translate strategic priorities into robust, buildable technical solutions.
• We build reusable frameworks, libraries, developer tooling, and platform components that accelerate AI development across multiple teams without creating unnecessary abstraction or complexity.
• We evaluate emerging AI technologies through practical prototypes, proof‑of‑concept builds, and production‑readiness assessments, then guide teams on implementation patterns and trade‑offs.
• We mentor engineers through practical technical coaching, pairing, code reviews, design feedback, documentation, and example‑setting as a senior individual contributor.

Technologies:

• AI
• AI Agents
• Azure
• Backend
• Cloud
• Docker
• Support
• Java
• Kubernetes
• Machine Learning
• MongoDB
• PostgreSQL
• Python
• Redis
• Security
• TypeScript
• Web

More:

We are Moodys Corporation, a global leader in ratings and integrated risk assessment, and we are transforming how the world sees risk by advancing AI that moves from insight to action. Our Innovation team builds next‑generation internal and external products powered by large language models, AI agents, machine learning, and natural language processing. We work collaboratively across the full AI lifecycle, from experimentation and prototyping through to large‑scale production deployment, while shaping reusable platforms, frameworks, and responsible AI practices. We offer an inclusive environment where curiosity, innovation, knowledge sharing, and continuous growth are valued, and we invite talented candidates to apply even if they do not meet every requirement.