Job description

About the Role: -

We are looking for a motivated LLM Engineer with 1–2 years of experience to help us build and deploy AI-powered applications using Large Language Models.

The ideal candidate should have practical experience with LLM APIs, Python, RAG, prompt engineering, embeddings, and AI application development. You should be comfortable experimenting with AI technologies and turning prototypes into reliable product features.

Key Responsibilities: -

• Build and integrate LLM-powered features into our applications.

• Work with LLM APIs such as OpenAI, Gemini, Anthropic, or similar platforms.

• Develop RAG (Retrieval-Augmented Generation) applications.

• Work with embeddings, vector databases, and semantic search.

• Design and improve prompts for accuracy, consistency, and performance.

• Implement function calling, structured outputs, and AI workflows.

• Develop APIs and backend services using Python.

• Evaluate LLM responses and improve accuracy and reliability.

• Debug and optimize LLM applications for performance, latency, and cost.

• Work with developers and product teams to integrate AI features into production.

• Stay updated with new LLM models, frameworks, and Generative AI technologies.

Required Skills:-

• 1–2 years of experience in software development, AI/ML, or Generative AI.

• Strong programming knowledge in Python.

• Hands-on experience with at least one LLM API such as:

OpenAI
Google Gemini
Anthropic
Open-source LLMs

• Understanding of Prompt Engineering.

• Practical understanding of RAG.

• Experience with embeddings and vector databases.

• Basic experience with REST APIs.

• Familiarity with Git and GitHub.

• Good problem-solving and debugging skills.

Good to Have:-

• Experience with LangChain, LlamaIndex, or LangGraph.

• Experience with vector databases such as Pinecone, Qdrant, Weaviate, Chroma, or pgvector.

• Experience with FastAPI or Flask.

• Basic knowledge of PostgreSQL or MongoDB.

• Familiarity with Docker.

• Experience deploying applications on AWS, Azure, or Google Cloud.

• Understanding of LLM evaluation and observability.

• Exposure to AI agents and tool/function calling.

We are looking for someone who:-

• Has actually built LLM/Generative AI projects, not just completed courses.

• Can independently build a small AI feature from idea to working prototype.

• Understands the difference between a simple chatbot and a production-grade LLM application.

• Is comfortable experimenting with different models and prompts.

• Can troubleshoot hallucinations, poor retrieval, and inconsistent LLM responses.

• Is eager to learn and keep up with the rapidly changing AI ecosystem.

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