Prompt Engineer

Prompt Engineer

Experience : 3–5 years

Location : Hyderabad (Onsite / Hybrid)

Job Description :

We are looking for a Prompt Engineer with strong software engineering expertise and hands-on experience working with Large Language Models (LLMs) and Agentic AI frameworks . The ideal candidate will design, test, and optimize prompts, retrieval pipelines, and agentic workflows that power intelligent, production-grade AI systems.

Key Responsibilities :

Design, prototype, and refine prompts for LLMs (OpenAI, Anthropic, Gemini, Llama, etc.).

Build and integrate LLM-based services via APIs with FastAPI (routers, DI, auth, rate limiting).

Implement Retrieval-Augmented Generation (RAG) workflows — document parsing, chunking, embeddings, vector search, and hybrid retrieval.

Develop agentic flows and state machines using LangGraph ; build chains / tools using LangChain .

Create and maintain testing frameworks for prompts, retrieval logic, and chain / tool performance (A / B testing, relevance, latency, and cost).

Manage vector database integrations (FAISS, Pinecone) and ensure retrieval accuracy.

Collaborate on end-to-end API development , CI / CD integration, and system monitoring.

Maintain documentation, best practices, and advocate for prompt engineering standards across teams.

Must-Have Skills :

3–5 years of software engineering experience with strong Python skills.

Proven hands-on experience with FastAPI , LLMs (OpenAI, Anthropic, Gemini, Llama), and function / tool calling .

Practical experience implementing RAG , embeddings , hybrid retrieval , and document chunking .

Proficiency with LangGraph and / or LangChain for agentic and chained workflows.

Experience with vector databases (FAISS, Pinecone, Weaviate) and document preprocessing .

Familiarity with Git , Docker , and CI / CD pipelines .

Strong communication and collaboration skills.

Nice-to-Have Skills :

Experience in LLM fine-tuning (LoRA, quantization) and monitoring prompt performance .

Knowledge of cloud deployment (AWS / Azure / GCP) and LLM observability (latency, cost tracking).

Prior exposure to regulated domains such as healthcare or life sciences.