About the role
structured by ORIWe're looking for a passionate AI Engineer to design, develop, and deploy production-grade AI applications powered by Large Language Models. You'll collaborate with a distributed team to build scalable AI solutions that solve real-world business challenges.
What you will do
- Design and develop AI-powered applications using OpenAI, Anthropic, Gemini, or open-source LLMs.
- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
- Develop AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, or LlamaIndex.
- Deploy scalable AI services using Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
- Collaborate with cross-functional teams in a remote environment to deliver AI features from concept to production.
What they are looking for
- Strong proficiency in Python.
- Experience building applications with LLM APIs.
- Hands-on experience with RAG, embeddings, and vector databases (Pinecone, Weaviate, ChromaDB, FAISS, or Milvus).
- Experience with LangChain, LangGraph, LlamaIndex, or similar AI frameworks.
- Familiarity with FastAPI or Flask for backend development.
- Understanding of prompt engineering, function calling, and agentic workflows.
- Experience with Git, Docker, CI/CD, and cloud deployment.
- Strong analytical and problem-solving skills.
Nice to have
- Experience with fine-tuning or deploying open-source LLMs.
- Knowledge of MCP (Model Context Protocol), AI workflow orchestration, or LLMOps.
- Familiarity with evaluation frameworks, guardrails, and observability tools.
- Experience building production-grade AI products.
Full posting text
We're looking for a passionate AI Engineer to design, develop, and deploy production-grade AI applications powered by Large Language Models. You'll collaborate with a distributed team to build scalable AI solutions that solve real-world business challenges.
Key Responsibilities
Design and develop AI-powered applications using OpenAI, Anthropic, Gemini, or open-source LLMs.
Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
Develop AI agents and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, or LlamaIndex.
Integrate AI capabilities into web applications and backend services.
Engineer prompts, evaluate model performance, and optimize for quality, latency, and cost.
Deploy scalable AI services using Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).
Implement monitoring, evaluation, and observability for production AI systems.
Collaborate with cross-functional teams in a remote environment to deliver AI features from concept to production.
Stay up to date with the latest advancements in Generative AI and apply them to product development.
Required Skills
Strong proficiency in Python.
Experience building applications with LLM APIs.
Hands-on experience with RAG, embeddings, and vector databases (Pinecone, Weaviate, ChromaDB, FAISS, or Milvus).
Experience with LangChain, LangGraph, LlamaIndex, or similar AI frameworks.
Familiarity with FastAPI or Flask for backend development.
Understanding of prompt engineering, function calling, and agentic workflows.
Experience with Git, Docker, CI/CD, and cloud deployment.
Strong analytical and problem-solving skills.
Preferred Qualifications
Experience with fine-tuning or deploying open-source LLMs.
Knowledge of MCP (Model Context Protocol), AI workflow orchestration, or LLMOps.
Familiarity with evaluation frameworks, guardrails, and observability tools.
Experience building production-grade AI products.
Seniority level: Entry level
Employment type: Full-time
Industries: Technology, Information and Media, IT Services and IT Consulting, and Software Development