Job Description
Are you ready to shape the future of artificial intelligence? Apex Data Systems is seeking a visionary Prompt Engineer to join our dynamic team in Omaha, Nebraska.
We are a cutting-edge technology firm focused on integrating Large Language Models (LLMs) into enterprise solutions. You will be the bridge between human intent and machine comprehension, crafting the precise instructions that drive our AI engines.
Why join us?
- Impactful Work: Directly influence how our AI interacts with millions of users.
- Modern Environment: Work with the latest tools and technologies in a collaborative, high-performance culture.
- Competitive Compensation: Top-tier salary and comprehensive benefits package.
Don't miss this opportunity to start tomorrow and lead the charge in AI innovation.
Responsibilities
- Develop, test, and refine complex prompts for Large Language Models (LLMs) to maximize accuracy, tone, and relevance.
- Collaborate closely with data scientists and software engineers to integrate AI models into production applications seamlessly.
- Conduct rigorous A/B testing and analyze model outputs to optimize user experience and reduce hallucinations.
- Document prompt engineering best practices and create libraries of reusable templates for the broader engineering team.
- Stay abreast of the latest advancements in NLP and LLM research to implement cutting-edge techniques immediately.
- Translate complex business requirements into technical prompt specifications and evaluation criteria.
Qualifications
- Bachelor’s degree in Computer Science, Linguistics, Cognitive Science, or a related technical field (or equivalent practical experience).
- 3+ years of experience working with AI, NLP, or Large Language Models (GPT, Claude, Llama, etc.).
- Proficiency in Python and familiarity with APIs such as OpenAI, Hugging Face, or Anthropic.
- Exceptional written communication skills with a keen eye for detail, nuance, and context.
- Strong analytical mindset with the ability to troubleshoot and debug complex prompt scenarios.
- Experience with fine-tuning models or RAG (Retrieval-Augmented Generation) architectures is a strong plus.