About the company
We are Qatar Insurance Company (QIC), the leading insurance provider in GCC. With a history dating back to 1964, we have established ourselves as a pioneer in the insurance industry, offering innovative solutions to meet the diverse needs of individuals and businesses.
Our vision is to make QIC group become the first Digital Ecosystem in the region by combining insurance and non-insurance services in one platform. Through our digital platforms, such as qic.online and the QIC app, we empower customers to manage their needs anytime and anywhere.
Our employees have been featured in Forbes 30 under 30, teach at online universities, serve on program committees of major IT conferences, and have previously worked at Yandex, Tinkoff, Avito, Ozon, and other leading tech companies.
About the position
We are looking for an experienced AI/ML Engineer to join our team and help design, build, and scale intelligent systems that power real business solutions. You will work across LLMs, Computer Vision, Speech AI, and AI Infrastructure, embedding advanced AI into core business processes and client-facing products.
Responsibilities
- LLM Development: Build chatbots, RAG systems, and automated document processing pipelines.
- Computer Vision: Implement OCR for insurance documents, damage assessment from photos, and identity verification.
- Speech AI: Develop transcription, voice analytics, and multilingual support solutions.
- AI Infrastructure: Deploy and monitor AI models, integrate with cloud AI services.
- Product Integration: Embed AI into business workflows, create AI APIs, and design user-friendly AI-powered experiences.
Requirements
- 3+ years of experience in AI/ML engineering with a focus on production systems
- Strong Python skills (advanced) + experience with FastAPI/Flask for API development
- Hands-on experience with LLM tools: OpenAI API, LangChain, Hugging Face Transformers
- Computer Vision expertise: OpenCV, PIL, YOLO, Detectron2 (or similar)
- Deep learning frameworks: PyTorch or TensorFlow for fine-tuning models
- Cloud experience: AWS / Azure / GCP AI services
- Docker & Kubernetes for AI application containerization and orchestration
Core AI/ML Competencies
- Prompt Engineering – designing effective prompts for LLMs
- Fine-tuning – adapting pre-trained models to business tasks
- RAG (Retrieval-Augmented Generation) – working with vector DBs and embeddings
- Computer Vision Pipelines – from preprocessing to inference
- Model Optimization – quantization, pruning, ONNX for faster inference
- Multimodal AI – building solutions that combine text + images
