مهندس ذكاء اصطناعي (AI Engineer)

Job Summary

The AI Engineer will design, develop, deploy, and scale AI-driven product features, collaborating with multiple teams to convert AI concepts into scalable production solutions.

Responsibilities

• Integrate AI/LLM capabilities using cloud services like AWS Bedrock and Azure OpenAI.
• Design and implement agentic workflows and LLM-driven features with frameworks such as LangGraph and LangChain.
• Develop Retrieval-Augmented Generation (RAG) pipelines, vector embeddings, and prompt engineering workflows.
• Convert AI prototypes into scalable, secure, maintainable, and tested production systems.
• Utilize AI-assisted development tools like GitHub Copilot and Claude Code ensuring code quality and security.
• Collaborate with Product Managers, Data Engineers, Software Engineers, and AI Researchers to address technical challenges.
• Assess and enhance AI models and workflows based on quality, reliability, latency, and performance metrics.

Must haves

• 3–7 years of experience in AI Engineering, Machine Learning Engineering, or Software Engineering with strong AI/ML skills.
• Practical experience building AI/LLM applications, including prompt engineering and LLM orchestration.
• Hands-on experience with AI orchestration frameworks such as LangGraph or LangChain.
• Strong Python programming skills with solid software engineering fundamentals.
• Experience with REST/gRPC APIs, data processing, debugging, testing, CI/CD, and Git.
• Strong analytical and problem-solving abilities, capable of working independently and in cross-functional teams.

Nice to haves

• Experience with AWS or Azure cloud platforms.
• Familiarity with vector databases like Milvus, Pinecone, or Qdrant.
• Knowledge of knowledge graphs, retrieval systems, and multimodal AI.
• Experience with Docker, Kubernetes, CI/CD practices, AI evaluation frameworks such as LangSmith or Phoenix, and LLM monitoring.

We refresh listings regularly, but some roles close early on the source platform.