We are looking for a highly skilled Prompt Engineering Specialist to design, optimize, and manage prompts for large language models (LLMs).

The ideal candidate will blend expertise in NLP, AI model behavior, and programming to maximize model accuracy, reliability, and output quality.

This role requires strong analytical thinking, creativity in prompt design, and hands-on experience with iterative testing and evaluation.

Key Responsibilities :

- Prompt Design & Optimization : Create, refine, and maintain high-performing AI prompts for diverse use cases.

- NLP Application : Apply linguistic principles (syntax, semantics, pragmatics) to improve contextual accuracy and clarity.

- Model Performance Tuning : Use techniques such as chain-of-thought prompting, few-shot learning, and role prompting to enhance responses.

- Testing & Evaluation : Conduct A/B testing, version control, and performance benchmarking to optimize results.

- Tool Integration : Leverage tools such as PromptFlow, n8n, and workflow automation platforms.

- Analytics & Insights : Analyze model outputs, identify improvement areas, and implement corrective strategies.

- Collaboration : Partner with AI engineers, data scientists, and product managers to align prompt strategies with business goals.

- Documentation : Maintain comprehensive records of prompt versions, evaluation results, and methodologies.

Required Qualifications :

- Strong proficiency in Python for testing, data analysis, and automation.

- Hands-on experience with OpenAI GPT or similar LLM platforms.

- Expertise in prompt engineering techniques (few-shot, chain-of-thought, role-based).

- Familiarity with A/B testing, evaluation metrics, and prompt versioning.

- Knowledge of workflow automation tools (PromptFlow, n8n).

- Strong analytical and troubleshooting skills.

Requirements :

Preferred Qualifications :

- 6 - 8 years in AI/ML, including 2+ years in Generative/Agentic AI.

- Experience with frameworks such as LangChain, LangGraph, AutoGen, CrewAI, Google ADK.

- Exposure to RAG (Retrieval-Augmented Generation) pipelines and vector databases (Pinecone, Weaviate, Milvus).

- Experience with MCP server implementation and agent-to-agent communication.

- Hands-on with fine-tuning, reinforcement learning, and chatbot development (Teams, Slack).

- Proficiency with SQL/NoSQL databases and cloud-native deployment (Docker, Kubernetes).