3 to 4 years of experience
Core skills: LLM behaviour, prompt design, system instructions, few-shot examples, structured outputs, context management and prompt iteration.
Technical skills: Python basics, JSON, API integration, basic SQL and understanding of tokens, context windows and model limitations.
Tools: OpenAI API, Azure OpenAI, Anthropic APIs, prompt playgrounds, LangChain, basic evaluation tools and Git.
Quality skills: Test prompt variations, create test datasets, evaluate hallucinations, relevance, consistency and instruction adherence.
Experience expectation: Develop reusable prompts and workflows for defined business use cases; document performance and failure cases.

58 years of experience
Core skills: Advanced prompt/system design, multi-step workflows, structured generation, tool use, multimodal prompting and prompt versioning.
Technical skills: Python, API integration, evaluation pipelines, dataset design and integration with RAG/agent architectures.
Tools: LLM evaluation frameworks,



LangSmith, prompt/version management, tracing and observability platforms; model APIs and orchestration frameworks.
Quality skills: Design repeatable benchmarks, adversarial test cases, regression tests and task-specific quality metrics.
Experience expectation: Establish prompt engineering standards, improve reliability across models and collaborate with engineering, product and domain teams.

Recognized certifications
AWS Certified AI Practitioner.
Microsoft Certified: Azure AI Fundamentals (AI-900; verify current exam availability).
Relevant cloud AI or generative AI credentials from AWS, Microsoft or Google Cloud.

Prompt engineering has no widely established skilled certification standard. Practical evaluation results and a demonstrable portfolio are particularly significant. explain this JD

📌 Prompt Engineer Bengaluru
🏢 Qapitol QA
📍 Bengaluru