Jr. AI Native Engineer

Overview

Build AI-native applications and intelligent digital products with multidisciplinary teams, combining strong software engineering fundamentals with modern AI capabilities such as LLMs, agentic systems, retrieval, APIs and cloud platforms.

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 you'll need

• 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.
• A demonstrated habit of exploring emerging technology and turning your curiosity into something tangible.

Nice to have

• 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.

Details

• Location: United States, remote.