About the role
We are looking for an AI Engineer with 2-4 years of experience in applied AI, large language models (LLMs), and retrieval-augmented generation (RAG). The ideal candidate will have hands-on experience building and deploying AI-powered applications, integrating LLMs into real-world products, and developing intelligent solutions to solve business problems.
Responsibilities:
- Design, develop, and deploy AI-powered applications using LLMs and generative AI technologies.
- Build and optimize RAG pipelines for document retrieval, knowledge extraction, and context-aware responses.
- Integrate LLM APIs such as OpenAI, Anthropic, Gemini, or open-source models into applications.
- Develop AI workflows, intelligent assistants, chatbots, and agent-based applications.
- Work with embedding models, vector databases, semantic search, and document chunking techniques.
- Implement prompt engineering, context optimization, and techniques to improve response quality.
- Evaluate and optimize AI solutions for accuracy, latency, scalability, and cost efficiency.
- Collaborate with backend and product teams to integrate AI capabilities into production systems.
- Follow best practices for AI evaluation, monitoring, testing, and responsible AI development.
Requirements:
- Experience: 2-4 years in AI engineering, applied AI, generative AI, or a related software engineering role.
- Programming: Strong proficiency in Python.
- LLMs and Generative AI: Hands-on experience with LLMs, LLM APIs, and generative AI applications.
- RAG: Experience building Retrieval-Augmented Generation pipelines and knowledge-based AI applications.
- AI Frameworks: Familiarity with LangChain, LlamaIndex, LangGraph, or equivalent frameworks.
- Vector Databases: Experience with Pinecone, FAISS, Chroma, Weaviate, Qdrant, or similar technologies.
- Machine Learning Fundamentals: Understanding of embeddings, semantic search, NLP, and model inference.
- Backend Development: Experience with FastAPI, Flask, REST APIs, or equivalent technologies.
- Applied AI: Ability to translate business requirements into practical, scalable AI solutions.
- Deployment: Familiarity with Git, Docker, cloud platforms, and production deployment practices.