About The Economist Group
Since 1843, The Economist Group has championed independence, excellence and openness, helping people understand and tackle the critical challenges shaping the world. Today, we are building on that legacy as a global media and information-services company powered by digital innovation, analytical rigour and evidence-based insight. Across our three businesses - The Economist, Economist Enterprise and Economist Education - we deliver trusted analysis and insights to individuals and organisations in more than 170 countries.
Technology, Product & Data
Our team of 450 colleagues across Technology, Product and Data is at the heart of The Economist Group's digital-first agenda. Together, we design, build and optimise the digital products, platforms and data capabilities that power our business and reach millions of people around the world.
Our AI Platform team builds the technology that powers AI-enabled products and services, for internal users and external customers. We design, build and operate the infrastructure, patterns and reusable capabilities that enable generative and agentic AI, natural language processing, predictive analytics and machine learning across the Group, from customer-facing features in our digital products to the internal tools used by colleagues across our four businesses.
Scope
We are seeking an Engineering Manager to lead our Applied AI and Data Science team: a multidisciplinary group of AI Engineers, ML Engineers, NLP Engineers and Data Scientists. This is a player-coach role. You will own the health, delivery and growth of the team while remaining a hands-on contributor to its work, writing and reviewing code, shaping architecture and stepping into implementation when it matters.
The team's work spans generative and agentic AI systems built on large language models, alongside the NLP and predictive models that sit behind our products and internal tools. You will turn the Group's AI ambitions into well-architected, production-grade systems, working in close partnership with the Head of AI and Data Engineering, product managers, data engineers and business stakeholders to decide what we build, why we build it, and how we know it is working. You will also champion the team's move to AI-native engineering, with coding agents used across the whole software development lifecycle.
Accountabilities
•Lead and manage the team. Provide day-to-day management, mentorship and career development, and build one team across engineering and data science disciplines.
•Own delivery. Own planning, resourcing, prioritisation and delivery with product management, balancing exploratory work against production commitments.
•Communicate progress. Keep leadership and stakeholders informed on progress, risks and trade-offs, and set realistic expectations of what AI can deliver.
•Stay hands on. Contribute directly to the team's deliverables: build, prototype, review code and models, and step into implementation when it matters.
•Contribute to technical and architectural decisions. Shape the architecture of our agentic AI, RAG, NLP and ML systems, covering agent design, model selection and build-versus-buy.
•Oversee the development of safe and reliable agents. Ensure our agents have well-scoped tool permissions, guardrails and human-in-the-loop controls, and are evaluated end to end before and after release.
•Ensure quality, security and compliance. Set standards for code quality, testing, evaluation, observability and cost, and keep AI systems secure, well-architected and compliant.
•Champion AI-native engineering. Embed AI coding agents across the SDLC of the team, from specification and design to code, tests and review, with clear standards for AI-generated code.
•Partner with others. Work with Product, Data Engineering, Security, Legal and the business units, challenging requirements and using metrics to define what "done" looks like.
•Hire and grow the team. Recruit, interview and onboard engineers and data scientists, shaping the team's skills mix to match the AI roadmap.
•Drive best practices and ways of working. Improve CI/CD, experiment tracking and the path to production, and promote the team's capabilities across the Group.
Selection Criteria
•A track record as an engineering manager or tech lead with direct people-management responsibility for AI/ML engineers, NLP engineers and/or data scientists.
•Experience balancing hands-on technical contribution with people leadership and delivery ownership.
•Strong hands-on AI/ML engineering background, with extensive Python experience and confidence reviewing code across LLM, agentic, NLP and classical ML systems.
•Hands-on experience with modern agent frameworks such as Strands Agents, LangGraph or the Claude Agent SDK, and with interoperability standards such as MCP and A2A.
•Strong cloud-native expertise on AWS, especially Amazon Bedrock and Bedrock AgentCore, alongside SageMaker, Lambda and Step Functions, with CDK or equivalent IaC.
•Hands-on LLMOps and MLOps experience, including agent and trajectory evaluation, tracing (e.g. OpenTelemetry), guardrails, cost monitoring and automated model pipelines.
•A clear understanding of agent security risks, such as prompt injection, data leakage and over-permissioned tools, and how to mitigate them.
•Day-to-day experience of AI-native software delivery, using coding agents such as Claude Code, Cursor or GitHub Copilot across planning, coding, testing and review.
•Able to set team standards for AI-assisted development, including context engineering for coding agents, review of AI-generated code and measuring its impact on delivery.
•A solid grounding in applied data science, enough to lead data scientists and constructively challenge their work.
•Excellent communication skills, able to work closely with product, design and engineering, and explain technical trade-offs to non-technical stakeholders.
•Strong product sense: comfortable interrogating and refining requirements rather than taking them as given, and able to spot when one doesn't hold up.
•Direct experience applying AI within a media, publishing, entertainment or other content-led industry.
•Proven experience building and operating agentic AI systems in production, including multi-agent orchestration, tool use via MCP, agent memory and agentic RAG.
•Depth in NLP, such as information extraction, classification, summarisation and semantic search, or fine-tuning LLMs.
•Experience building shared AI platforms or reusable capabilities consumed by multiple product teams, such as internal MCP servers or agent tooling.
•Experience with vector search at scale, using tools such as OpenSearch, Amazon S3 Vectors or pgvector.
•Familiarity with AI governance and regulation, and with putting responsible AI practices into day-to-day engineering.
•An advanced degree in computer science, data science, linguistics or a related quantitative field, or equivalent work experience.
Working Arrangements
The majority of our roles operate on a hybrid working pattern, with 3+ days office attendance required.
AI usage for your application
We are an innovative organisation that encourages the use of technology. We recognise that candidates may utilise AI tools to support with their job application process. However, it is essential that all information you provide truthfully and accurately reflects your own experience, skills, and qualifications.
Benefits
Our benefits package is designed to support your wellbeing, growth, and work-life balance. It includes a highly competitive pension or 401(k) plan, private health insurance, and 24/7 access to counselling and wellbeing resources through our Employee Assistance Program. We also offer a range of lifestyle benefits, including our Work From Anywhere program, which allows you to work from any location where you have the legal right to do so for up to 25 days per year. In addition, we provide generous annual and parental leave, as well as dedicated days off for volunteering and even for moving home. You will also be given free access to all The Economist content, including an online subscription, our range of apps, podcasts and more.
