Hiring!!!!

We are looking for a Prompt Engineer with 2+ years of relevant experience and also having expereince working with LLM Applications.

Notice Period: Immediate joiner to 15 days

Location: Hyderabad

Technical Skills

• Prompt Engineering: 2+ years of hands-on experience designing, optimizing, testing, and maintaining prompts for production GenAI/LLM applications.
• Advanced Prompting: Expertise in zero-shot, few-shot, system/role prompting, structured outputs, prompt templates, multi-step prompting, dynamic prompts, and instruction hierarchy.
• Context Engineering: Experience optimizing instructions, retrieved content, conversation history, examples, metadata, and tool outputs to improve LLM response quality.
• LLM Knowledge & Platforms: Strong understanding of LLM behavior, hallucinations, tokenization, context windows, model parameters, latency, and cost. Hands-on experience with OpenAI/Azure OpenAI, Claude, Amazon Bedrock, or Gemini.
• Prompt Optimization & Evaluation: Ability to troubleshoot prompt failures and build evaluation frameworks using golden datasets, scoring rubrics, regression tests, A/B testing, and quality metrics such as accuracy, relevance, groundedness, faithfulness, and hallucination rate.
• RAG & Grounding: Understanding of embeddings, chunking, semantic search, retrieval quality, context relevance, and citation-based grounding.
• Safety & Conversation Design: Experience testing prompt injection, jailbreaks, data leakage, harmful outputs, and out-of-scope queries; designing multi-turn conversations, clarification strategies, and fallback handling.
• Agentic AI & Frameworks: Familiarity with function calling, tool use, MCP, multi-agent workflows, LangChain, LangGraph, Semantic Kernel, or Prompt Flow.
• Python & LLM API Integration: Strong Python skills, REST APIs/SDKs, JSON/structured outputs, streaming, token management, retries, and model configuration.
• Prompt Lifecycle & Observability: Experience with prompt versioning, Git, documentation, CI/CD, regression testing, production monitoring, LLM traces, token usage, and latency analysis.
• Business & Communication Skills: Ability to translate business requirements into prompts, evaluation criteria, expected outputs, and measurable quality improvements.

