Key Responsibilities
- Integrate large language models (OpenAI, Anthropic, open-source models) into product workflows via APIs
- Design, test, and iterate on prompts to consistently produce correct outputs for business use cases
- Build context engineering pipelines including retrieval, chunking, embeddings, and vector store integration (RAG)
- Implement output validation, guardrails, and structured output handling such as JSON schemas and function calling
- Develop evaluation frameworks and test harnesses to measure model accuracy and detect regressions
- Optimize for cost, latency, and token efficiency across model calls for scalable deployment
Requirements
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or a related field
- Proven experience in AI development, particularly with large language models and API integrations
- Strong proficiency in Python programming language
- Hands-on experience with LLM APIs such as OpenAI, Anthropic, or open-source models
- Familiarity with prompt engineering, context management, and retrieval-augmented generation (RAG)