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?