AI Engineer

Job Summary

The AI Engineer role involves designing, developing, and deploying AI-based solutions with a focus on large language models and related technologies. The position requires hands-on work with AI applications, model benchmarking, optimization, and deployment.

Responsibilities

• Develop AI applications using LLMs, Retrieval-Augmented Generation, and Model Context Protocol.
• Implement features like function calling, agent orchestration, and tool integration.
• Evaluate AI models through LLM-as-a-Judge, few-shot methods, and automated tests.
• Experiment with model optimization and quantization techniques.
• Containerize and deploy applications using Docker and Docker Compose.
• Utilize Hugging Face Transformers for embeddings, tokenization, and text classification.
• Work with vector databases for semantic search and retrieval tasks.
• Apply image processing and computer vision techniques using OpenCV.

Must haves

• 1 to 2 years of practical experience in AI/ML engineering.
• Strong Python programming skills.
• Familiarity with PyTorch or TensorFlow frameworks.
• Understanding of ML/NLP concepts including LLMs and RAG workflows.
• Experience in Unix/Linux environments.
• Exposure to asynchronous frameworks such as FastAPI and asyncio.
• Comfortable working with Docker and containerized setups.
• Experience with Git and version control systems.

Nice to haves

• Knowledge of quantization methods like GPTQ and AWQ.
• Experience with Hugging Face Transformers for NLP tasks.
• Familiarity with vector databases such as Qdrant or ChromaDB.
• Experience in computer vision using OpenCV.
• Exposure to databases including MariaDB, PostgreSQL, and SQLite.
• Familiarity with queuing systems like RabbitMQ and Redis Queue.
• Understanding of MCP and agent orchestration tools like LangChain, LlamaIndex, and Haystack.

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