Senior Prompt Engineer

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• Design, develop, and maintain prompt frameworks, including system prompts, few-shot examples, role-based prompts, and reasoning workflows for production-grade LLM applications.
• Build and manage automated evaluation frameworks to measure model performance, accuracy, latency, and regression across releases.
• Conduct structured A/B testing across prompt variations, model versions, and configuration settings to optimize task-specific outcomes.
• Convert product requirements and edge-case scenarios into effective prompt instructions, personas, constraints, and guardrails.
• Partner with ML engineers and product teams to determine when prompt engineering is sufficient versus when fine-tuning, RAG, or other AI architectures are required.
• Create and maintain a centralized prompt repository with version control, documentation, and performance benchmarks for organizational reuse.
• Lead red-teaming and adversarial testing exercises to identify jailbreak risks, hallucinations, and model vulnerabilities.
• Define evaluation criteria, annotation guidelines, and quality standards to ensure consistency, safety, and reliability of AI-generated outputs.
• Mentor engineers and stakeholders on prompt engineering best practices, evaluation methodologies, and the capabilities and limitations of modern LLMs.
• Present prompt strategies, benchmark results, and trade-off analyses to product, engineering, and leadership teams.
• Apply advanced prompting techniques, including chain-of-thought, zero-shot, few-shot, and role-based prompting.
• Drive prompt testing, evaluation, benchmarking, and continuous optimization efforts.
• Improve AI response quality through systematic assessment, tuning, and refinement.
• Manage context handling and prompt orchestration for complex AI workflows.

• Strong programming and scripting skills in one or more modern programming languages(C#, Python, Javascript).
• Experience building automation, evaluation pipelines, APIs, or AI-powered applications using enterprise-grade development practices.
• Hands-on experience with LLM platforms, prompt engineering, model evaluation, and AI application development.
• Familiarity with prompt orchestration frameworks, vector databases, RAG architectures, and AI agent workflows.
• Understanding of data analysis, experimentation, benchmarking, and performance optimization.
• Experience with version control systems, CI/CD pipelines, and cloud platforms.
• Strong knowledge of REST APIs, JSON, and system integration patterns.
• Ability to collaborate effectively with software engineers, data scientists, and product teams to deliver production-ready AI solutions.

• 4-7 years of combined experience in NLP, AI/ML products, software development, technical writing, or related fields.
• At least 2 years of direct, hands-on prompt engineering experience with production LLM applications.
• Proven track record of owning and managing prompt systems end-to-end, from design and implementation through monitoring and optimization in production.

Regards,

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