Role & responsibilities

Technical Skills (Must Have)

- Prompt Engineering 2+ years: Strong hands-on experience designing, optimizing, testing, and maintaining prompts for production GenAI/LLM applications.
- Advanced Prompting Techniques: Expertise in zero-shot, few-shot, role/system prompting, structured prompting, prompt templates, multi-step prompting, and dynamic prompt construction.
- Context Engineering: Strong ability to design and optimize instructions, context, examples, retrieved information, conversation history, and tool outputs provided to LLMs.
- Generative AI & LLMs: Strong understanding of LLM behavior, capabilities, limitations, hallucinations, context windows, token usage, model parameters, and reasoning behavior.
- LLM Platforms: Hands-on experience with platforms/models such as Azure OpenAI/OpenAI, Anthropic Claude, Amazon Bedrock, or Google Gemini.
- Prompt Optimization: Ability to systematically analyze poor responses, identify prompt failure patterns, and improve accuracy, relevance, consistency, and instruction adherence.
- LLM Evaluation: Strong experience defining evaluation criteria, scoring rubrics, golden datasets, test scenarios, regression tests, and A/B testing for prompts.
- Response Quality Assessment: Experience measuring accuracy, groundedness, relevance, completeness, consistency, hallucination, and citation quality.
- RAG & Grounding: Valuable understanding of how retrieved context, chunking, relevance, citations, and grounding affect LLM responses. Deep implementation knowledge is not required.
- Prompt Safety & Guardrails: Experience designing and testing prompts against prompt injection, jailbreaks, sensitive-data exposure, harmful responses, and out-of-scope questions.
- Conversation Design: Ability to design system instructions, multi-turn conversations, clarification strategies, fallback responses, and effective human-AI interactions.
- Business Requirement Translation: Ability to work with business SMEs, understand domain requirements, and translate them into prompts, evaluation criteria, and expected response behavior.
- Prompt Lifecycle & Governance: Experience with prompt versioning, documentation, change tracking, regression testing,



and managing prompts across development, QA, and production environments.
- Analytical & Communication Skills: Strong analytical skills to identify LLM response patterns and clearly communicate findings and recommendations to business and technical teams.
- Prompt Engineering – 2+ years: Strong hands-on experience designing, optimizing, testing, and maintaining prompts for enterprise-grade Generative AI/LLM applications.
- Advanced Prompting Techniques: Expert knowledge of zero-shot ,few-shot, role/system prompting, chain-of-thought alternatives, structured prompting, prompt templates, multi-step prompting, dynamic prompts, and instruction hierarchy.
- Context Engineering: Strong experience designing and optimizing context including system instructions, retrieved content, conversation history, examples, tool outputs, metadata, and user inputs to improve LLM performance.
- Generative AI & LLM Expertise: Strong understanding of LLM architecture and behavior, tokenization, context windows, temperature/top-p, hallucinations, reasoning limitations, model capabilities, latency, and cost considerations.
- LLM Platforms & Models: Hands-on experience with Azure OpenAI, OpenAI, Anthropic Claude, Amazon Bedrock, and/or Google Gemini, including model selection and prompt optimization across different models.
- Prompt Optimization & Troubleshooting: Ability to systematically analyze LLM responses, identify prompt failure patterns, perform root-cause analysis, and optimize prompts for accuracy, relevance, consistency, and instruction adherence.
- LLM Evaluation & Testing: Strong experience creating evaluation frameworks, golden datasets, evaluation criteria, scoring rubrics, test scenarios, regression suites, benchmark datasets, and A/B testing for prompts and LLM applications.
- LLM Quality Metrics:



Hands-on experience evaluating accuracy, relevance, groundedness, faithfulness, completeness, consistency, hallucination rate, citation quality, and instruction-following.
- RAG & Grounding: Strong understanding of RAG concepts including retrieval quality, chunking strategies, embeddings, semantic search, context relevance, grounding, citations, and context optimization. Implementation experience is preferred.
- Prompt Safety & Guardrails: Experience designing and testing prompts against prompt injection, jailbreaks, data leakage, sensitive information exposure, hallucinations, malicious inputs, and out-of-scope requests.
- Agentic AI & Tool Calling: Experience designing prompts/instructions for AI agents, function calling, tool use, MCP, multi-agent workflows, planning, tool selection, and tool-result handling.
- Conversation & Interaction Design: Ability to design system instructions, multi-turn conversations, clarification strategies, fallback handling, error recovery, and human-AI interaction flows.
- Prompt Engineering Frameworks: Experience with frameworks/tools such as LangChain, LangGraph, Semantic Kernel, Prompt Flow, or equivalent GenAI orchestration frameworks.
- Programming & Automation: Strong Python skills for prompt experimentation, evaluation, automation, data processing, and integration with LLM APIs/SDKs.
- LLM API Integration: Hands-on experience working with REST APIs, SDKs, structured outputs/JSON, function calling, streaming, token management, retries, and model configuration.
- Prompt Lifecycle & Governance: Experience with prompt versioning, documentation, Git-based change management, experiment tracking, regression testing, CI/CD integration, and promotion across DEV/QA/PROD.
- Prompt Observability: Experience analyzing LLM traces, prompts, responses, token consumption, latency, errors, and evaluation results using tools such as Azure AI Foundry, LangSmith, Prompt Flow, or equivalent platforms.
- Business Requirement Translation: Ability to translate business requirements and use cases into system prompts, user prompts, context strategies, evaluation criteria, and expected LLM behavior.

📌 Prompt Engineer (Hyderabad)
🏢 Brace infotech
📍 Hyderabad