Jr. AI Native Engineer

Role overview Build intelligent digital products by combining solid software engineering with language models, retrieval, and agent-based workflows. The role suits an early-career engineer who enjoys experimenting, turning prototypes into dependable applications, and learning how emerging AI capabilities can solve practical problems.

Responsibilities - Design, prototype, and ship AI-enabled applications and user experiences for real-world use cases. - Build with language models, generative AI APIs, and agent architectures using Python, TypeScript/JavaScript, or similar languages. - Create retrieval solutions using embeddings, semantic search, vector stores, and business data. - Connect AI features to APIs, databases, cloud services, and existing enterprise systems. - Test and evaluate AI behavior for quality, accuracy, reliability, response time, and cost; move promising experiments toward scalable solutions. - Apply security, privacy, observability, responsible AI, and human review practices, and share findings with colleagues.

Requirements - Two to four years of professional software engineering or application development experience. - Strong programming ability in Python, TypeScript/JavaScript, or another modern language, with fundamentals in architecture, APIs, testing, debugging, version control, and CI/CD. - Hands-on generative AI project experience, including substantial prototypes, personal work, open-source contributions, or production applications. - Practical familiarity with language-model APIs, prompting, context handling, structured outputs, and choosing models for a task. - Exposure to retrieval techniques such as embeddings, semantic search, or vector databases, and to agents, tool calling, or workflow orchestration. - Curiosity about emerging technology, paired with an understanding that dependable products need more than a successful demo.

Nice to have Experience with frameworks such as LangChain, LangGraph, Semantic Kernel, or LlamaIndex; vector search tools; a major cloud platform; AI coding assistants; or methods for evaluating model outputs. Personal AI projects, hackathons, and community contributions are also relevant.

Benefits and work setup The role involves collaboration across engineering, data, experience, and strategy teams, with opportunities for continued learning and experimentation on client and business challenges.