AI Engineer

📋 Description

• Design and implement traditional ML and LLM-based systems and applications
• Optimize model inference performance and cost efficiency
• Fine-tune foundation models for specific use cases and domains
• Implement diverse prompt engineering strategies
• Build robust backend infrastructure for AI-powered applications
• Implement and maintain MLOps pipelines for AI lifecycle management

🎯 Requirements

• 4–8 years of experience in LLMs, Backend Engineering, and MLOps
• LLM Expertise
• Model Fine-tuning: parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters)
• Inference Optimization: quantization, pruning, caching, and serving optimizations
• Prompt Engineering: prompt design, few-shot learning, RAG
• Model Evaluation: experience with AI evaluation frameworks and metrics

🎁 Benefits

• Competitive salary with strong insurance package
• Learning and development resources