Jr. AI Native Engineer

Description

The company builds digital products and customer experiences for clients, using technology, creativity and experience to solve business problems across industries.

The team works in a global, borderless setup that supports collaboration and an environment built for learning, impact and growth.

AI roles sit alongside engineering, cloud, data and digital experience teams, so AI is part of the wider technology organisation rather than a separate specialism.

This role suits someone who has spent the last few years building engineering fundamentals and has explored AI on their own, before it was part of their job.

Responsibilities

• Design, prototype and build AI-native applications and experiences for real use cases, working with multidisciplinary teams.
• Combine solid software engineering with AI across large language models, agentic systems, retrieval, APIs, cloud platforms and new development tools.
• Build applications on large language models, generative AI APIs and agent architectures.
• Write high-quality code in Python, TypeScript/JavaScript or a similar modern language.
• Work with Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini and open-source models.
• Build RAG and knowledge retrieval using embeddings, semantic search, vector databases and enterprise data.
• Try out AI agents, tool or function calling, structured outputs, orchestration and multi-step workflows.
• Connect AI solutions to APIs, databases, existing platforms and enterprise systems.
• Prototype quickly, test assumptions, and scale the experiments that prove useful.
• Create ways to evaluate and test AI systems for quality, accuracy, reliability, latency and cost.
• Use AI-assisted tools to rethink how software is designed, built, tested and maintained.
• Apply good practice on security, privacy, responsible AI, observability and human oversight.
• Share what you learn and help shape AI engineering practice across the company.
• Help the team find where AI is next useful. You do not need every answer, but you should want to find them.

Requirements

• 2, 4 years of professional software engineering or application development experience.
• Strong programming in Python, TypeScript/JavaScript or another modern language.
• Solid fundamentals: application architecture, APIs, testing, debugging, version control and CI/CD.
• Hands-on generative AI work, from client projects, production applications, prototypes, internal innovation, hackathons, open-source contributions or substantial personal projects.
• Practical experience with LLMs and model APIs, including prompting, context management, structured outputs and model selection.
• Familiarity with RAG, embeddings, semantic search or vector databases.
• Exposure to agents, function or tool calling, or AI workflow orchestration.
• Experience connecting applications to APIs, data sources, databases and cloud services.
• Understanding that a strong AI demo and a dependable AI product are very different challenges.
• Above all, a steady habit of exploring new technology and turning curiosity into something tangible.
• You might have built your own AI application, agent, copilot or developer tool.
• You might have worked with several models and learned where each performs well and where it falls short.
• You might have built RAG solutions on real-world or enterprise data.
• You might have explored agentic architectures, tool use or multi-agent workflows.
• You might have worked with frameworks such as Semantic Kernel, LangChain, LangGraph or LlamaIndex.
• You might have used Azure AI Search, Elasticsearch, pgvector, Pinecone, Qdrant or Weaviate.
• You might have built applications on Azure, AWS or Google Cloud.
• You might have used GitHub Copilot, Claude Code or Cursor in your engineering workflow.
• You might have created evaluations or testing approaches for AI outputs.
• You might have taken part in AI hackathons, internal innovation initiatives or open-source communities.
• You might have spotted a newly released AI capability and wanted to try it straight away.
• Specific frameworks change often, so the company cares more about how you learn, what you have built and what you have discovered.

Benefits

• You will collaborate with people across engineering, data, experience and strategy.
• You will work on client and business challenges where new AI capabilities can add value.
• You will have room to experiment, learn and grow, and to help define what AI-native engineering looks like at the company.