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Job Title: Senior Prompt Engineer
Location: New York, NY
Job Nature: Full Time
Duration: Long Term
Job Summary: We are looking for an experienced Senior Prompt Engineer to design, develop, test, and optimize prompts and interaction strategies for Large Language Models (LLMs) and Generative AI applications.
The Senior Prompt Engineer will work closely with AI/ML Engineers, Data Scientists, Software Engineers, Product Managers, Architects, and business stakeholders to develop reliable, scalable, and production-ready AI solutions.
The ideal candidate will have strong hands-on experience with Prompt Engineering, LLMs, Generative AI, RAG, prompt evaluation, AI agents, and Python, with the ability to translate complex business requirements into effective AI instructions, workflows, and evaluation frameworks. Modern production prompt engineering increasingly combines prompts with RAG, structured outputs, tool calling, agent orchestration, evaluation, and version management.
Key Responsibilities
- Design, develop, test, and optimize prompts for LLM-powered and Generative AI applications.
- Create reusable system prompts, prompt templates, and multi-step prompt workflows.
- Apply techniques such as zero-shot, few-shot, prompt chaining, and structured prompting to improve model outputs.
- Design prompts for structured outputs, including JSON and schema-based responses.
- Develop and optimize prompts for Retrieval-Augmented Generation (RAG) applications.
- Work with engineering teams to improve retrieval context, grounding, response relevance, and overall RAG performance.
- Design prompting strategies for AI agents and agentic workflows, including tool/function calling.
- Build systematic prompt evaluation and testing frameworks to measure accuracy, relevance, consistency, robustness, and quality.
- Create evaluation datasets, test cases, and benchmarks for different GenAI use cases.
- Identify and reduce hallucinations, irrelevant responses, and inconsistent model behavior.
- Perform prompt experimentation and comparative evaluation across different LLMs and model versions.
- Optimize prompts for quality, latency, token utilization, and cost efficiency.
- Maintain prompt versioning, documentation, reusable prompt libraries, and engineering standards.
- Collaborate with developers to integrate prompts into production applications and APIs.
- Work with AI/ML teams to determine when to use prompt engineering, RAG, or model customization/fine-tuning.
- Conduct adversarial testing and identify potential prompt injection, safety, and reliability issues.
- Define best practices and reusable standards for prompt engineering across GenAI applications.
- Monitor production AI behavior and continuously improve prompts based on evaluation results and user feedback.
Mentor junior engineers and provide technical guidance on prompt engineering and GenAI development.
Continuously identify opportunities to improve development practices, code quality, performance, and engineering efficiency.
