AI Engineer

Summary:

Currently we are looking for an AI Engineer will play a critical role in designing and implementing cutting-edge AI solutions that are adaptable for real-world applications. This position is essential for the team as it focuses on optimizing processes and enhancing data models to meet the demands of a collaborative, international environment.

Main Responsibilities:

• Design and implement production-grade AI models optimized for real-world applications.
• Create efficient data indexing architectures utilizing RAG, LiteRAG, or equivalent frameworks.
• Drive cross-functional collaboration to enhance internal systems and workflows.
• Develop and deploy scalable APIs for advanced data retrieval and processing.
• Establish robust quantitative and qualitative testing frameworks for AI solution evaluation.

Key Requirements:

• Advanced Python expertise with demonstrated proficiency in AI/ML libraries.
• Specialized knowledge in optimizing data indexing structures for AI search applications.
• Extensive experience working with Large Language Models (LLMs) and fine-tuning techniques.
• Proven track record building autonomous AI agents and sophisticated workflow systems.
• Demonstrated success integrating AI solutions with traditional software applications.
• Expert-level API development skills with emphasis on performance and scalability.

Nice to Have:

• Solid understanding of reinforcement learning and supervised fine-tuning for LLMs.
• Docker containerization expertise for AI application deployment.
• Kubernetes orchestration experience for managing complex AI workloads.
• Knowledge of Azure or Google Cloud Platforms, particularly with AI/ML services.
• Advanced text analysis capabilities including semantic understanding and NLP techniques.
• Familiarity with popular data analysis Python packages like NumPy, Pandas, and AI agent frameworks like PydanticAI.
• Prior experience in Machine Learning and Data Science.

Other Details:

• Location: International Team
• Commitment: Long-term commitment for mutual benefit
• Working Environment: Fewer meetings, more product iterations
• Dataset Size: Large dataset availability