AI Engineer

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.