Prompt Engineer

Brace Technologies Pvt LTD; Hyderabad; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

Brace Technologies Pvt LTD in Hyderabad, India, seeks an experienced Prompt Engineer to design and optimize prompts for enterprise GenAI/LLM applications. The role emphasizes advanced prompting techniques, context engineering, RAG grounding, evaluation frameworks, and governance across the prompt lifecycle.

You will collaborate with AI engineers and SMEs to translate business requirements into robust prompts and measurable outcomes.

Qualifications

• Must have hands-on experience designing, optimizing, testing, and maintaining prompts for enterprise GenAI/LLM applications.
• Strong knowledge of prompting techniques including zero-shot, few-shot, role/system prompting, and structured prompts.
• Experience with evaluating prompts, defining rubrics, golden datasets, and A/B testing.

Skills

Prompt Engineering

Advanced Prompting Techniques

Context Engineering

Generative AI & LLMs

LLM Platforms

Prompt Optimization

LLM Evaluation

Response Quality Assessment

RAG & Grounding

Prompt Safety & Guardrails

Conversation Design

Business Requirement Translation

Prompt Lifecycle & Governance

Analytical & Communication Skills

Job description

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: Good 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.