The opportunity
At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.
We are proud of:
- The work we do and the innovation we drive
- Our values of share, care and dare
- A workplace culture that fosters creativity, diversity and autonomy
- Our borderless, global framework, which enables seamless collaboration
Why Valtech?
The role
What you'll do
- Design, build and ship AI-native applications and experiences for real-world use cases.
- Build applications using large language models, generative AI APIs and agentic architectures.
- Develop high-quality software using Python, TypeScript/JavaScript or comparable modern languages.
- Work with models and platforms such as Azure OpenAI, OpenAI, Anthropic Claude, Google Gemini and open-source models.
- Build RAG and knowledge-retrieval solutions using embeddings, semantic search, vector databases and enterprise data.
- Experiment with AI agents, tool/function calling, structured outputs, orchestration and multi-step workflows.
- Integrate AI solutions with APIs, databases, existing platforms and enterprise systems.
- Rapidly prototype new ideas, test assumptions and turn promising experiments into scalable solutions.
- Build approaches for evaluating and testing AI systems, considering quality, accuracy, reliability, latency and cost.
- Use AI-assisted engineering tools to explore new ways of designing, developing, testing and maintaining software.
- Apply sound engineering practices around security, privacy, responsible AI, observability and human oversight.
- Share what you're learning and contribute to the evolving AI engineering practices across Valtech.
What we're looking for
- 2–4 years of professional software engineering or application development experience.
- Strong programming experience with Python, TypeScript/JavaScript or another modern programming language.
- Solid software engineering fundamentals, including application architecture, APIs, testing, debugging, version control and CI/CD.
- Hands-on experience building with generative AI, whether through client work, production applications, prototypes, internal innovation, hackathons, open-source contributions or substantial personal projects.
- Practical experience working 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/tool calling or AI workflow orchestration.
- Experience connecting applications to APIs, data sources, databases and cloud services.
- An understanding that building a great AI demo and building a dependable AI product are two very different challenges.
- Most importantly, a demonstrated habit of exploring emerging technology and turning your curiosity into something tangible.
We'd love it if you've...
- Built your own AI application, agent, copilot or developer tool.
- Experimented with multiple models and learned where each performs well, and where it doesn't.
- Built RAG solutions using real-world or enterprise data.
- Explored agentic architectures, tool use or multi-agent workflows.
- Worked with frameworks such as Semantic Kernel, LangChain, LangGraph or LlamaIndex.
- Used technologies such as Azure AI Search, Elasticsearch, pgvector, Pinecone, Qdrant or Weaviate.
- Built applications on Azure, AWS or Google Cloud.
- Used tools such as GitHub Copilot, Claude Code or Cursor as part of your engineering workflow.
- Created evaluations or testing approaches for AI outputs.
- Participated in AI hackathons, internal innovation initiatives or open-source communities.
- Seen a newly released AI capability and thought,“I need to try that.”
You are
You don't just read about AI. You build with it. You're comfortable moving from an idea to a prototype and learning through experimentation.
New models, multimodal experiences, agents, reasoning systems and AI development tools genuinely interest you. You're energized by a technology landscape where the playbook is still being written.
You care about what happens after the demo. Reliability, maintainability, architecture, security and testing matter to you.
You understand that AI isn't the answer to every problem. You're interested in finding where it creates genuine value, not adding AI for AI's sake.
You won't know every new model, framework or technique. That's okay. What matters is being able to say:“I haven't worked with that yet. Let's figure it out.”