Prompt Engineer

Title: Prompt Engineer

Location: Jersey City NJ(Hybrid)

Type: Contract

Primary ownership:

• Prompt patterns system instructions response templates and conversation policies for LLM use cases.
• Prompt testing versioning evaluation and quality improvement workflows.
• Prompt-security and output-quality analysis for RAG assistants copilots and agents.

Key responsibilities:

• Design prompts for chatbots copilots RAG systems document analysis summarization workflow agents and knowledge assistants.
• Develop system prompts few-shot examples tool-use instructions response formats escalation logic and conversation policies.
• Optimize prompts for accuracy relevance groundedness tone compliance latency token efficiency and repeatability.
• Build reusable prompt libraries and templates aligned to enterprise standards and business domains.
• Evaluate prompt performance using metrics such as task success groundedness hallucination rate completeness safety and user satisfaction.
• Partner with engineers to implement prompt versioning testing deployment and monitoring in production systems.
• Support RAG quality by assessing retrieval context chunking quality source citation behavior and response synthesis.
• Conduct adversarial testing for prompt injection jailbreaks instruction conflicts sensitive-data leakage and unsafe outputs.

Must-have candidate profile:

• Strong understanding of LLM behavior prompt design tokenization context windows RAG embeddings and model limitations.
• Hands-on experience with OpenAI APIs Azure OpenAI Anthropic LangChain LlamaIndex Semantic Kernel or similar platforms.
• Ability to debug LLM outputs using structured testing error analysis and iterative refinement.
• Strong writing analytical communication and stakeholder-management skills.
• Understanding of prompt-security risks including prompt injection jailbreaks data leakage and hallucination.

Preferred experience:

• Background in NLP conversational AI UX writing technical writing product design knowledge management or business analysis.
• Experience in financial services legal compliance risk operations customer support or enterprise knowledge domains.
• Familiarity with prompt registries A/B testing human review workflows and evaluation tooling.
• Initial screening questions
• Show how you improved a weak LLM output through prompt design and testing.
• How do you evaluate prompt performance beyond subjective quality
• How do you prevent prompt injection or instruction conflicts
• How do you work with engineers to move prompts into production safely
• How do you balance output quality latency and token cost