Job Description
Join NeuraTech Innovations' AI Excellence team as a Senior Prompt Engineer and revolutionize how humans interact with artificial intelligence. We're seeking a creative technologist to design, optimize, and deploy advanced prompt strategies that power our next-gen conversational AI systems. This hybrid role offers the opportunity to shape the future of human-AI collaboration while working alongside world-class researchers in our state-of-the-art Boston innovation hub.
You'll architect prompt frameworks that maximize LLM performance, conduct rigorous A/B testing of prompt variations, and develop proprietary techniques to reduce hallucinations and enhance response accuracy. The ideal candidate thrives at the intersection of linguistics, machine learning, and user experience design.
Responsibilities
- Design and implement sophisticated prompt engineering strategies for production LLM applications
- Develop and maintain prompt libraries with version control and performance tracking
- Collaborate with ML engineers to fine-tune model behavior through iterative prompt refinement
- Conduct systematic prompt testing to optimize for accuracy, safety, and user satisfaction
- Create documentation and best practices for prompt engineering across the organization
- Research emerging prompt techniques and integrate cutting-edge methodologies
- Partner with product teams to align prompt strategies with user experience goals
Qualifications
- Bachelor's degree in Computer Science, Linguistics, Cognitive Science, or related field (Master's preferred)
- 3+ years of hands-on prompt engineering experience with production LLMs
- Proven expertise in designing prompts for OpenAI GPT, Claude, or equivalent models
- Strong understanding of transformer architectures and attention mechanisms
- Experience with Python and prompt engineering frameworks (LangChain, Hugging Face)
- Portfolio demonstrating successful prompt optimization projects
- Exceptional written communication and analytical problem-solving abilities
- Familiarity with MLOps practices for prompt deployment and monitoring