Senior AI Engineer, ML & Model Quality

Senior AI Engineer, ML & Model Quality Important Information This role is with AIden Risk. Before applying, please note: Location: Onsite at our Islamabad office.Hiring Market: We are currently hiring Islamabad based candidates only.Working Hours: 7:00 PM to 4:00 AM (PKT).Please apply only if you are comfortable with the above requirements. About the Role We are looking for a Senior AI Engineer to lead the development and evaluation of machine learning models that power our AI products. You will replace rule based systems with supervised learning models, improve model quality, build production ready RAG pipelines, and drive research for self hosted AI inference. This role is ideal for someone with strong experience in applied machine learning, production AI systems, and end to end ownership of the ML lifecycle. Key Responsibilities Design, train, and deploy supervised machine learning models.Build and optimize production grade RAG pipelines.Develop evaluation frameworks and benchmarks to measure model performance.Own the complete ML lifecycle, including data collection, labeling, preprocessing, training, and deployment.Improve model accuracy through feature engineering, retrieval optimization, and experimentation.Work with LLMs to build reliable AI powered solutions.Collaborate with engineering teams to deploy scalable ML solutions into production.Continuously evaluate and improve model quality, performance, and inference costs. Requirements 5+ years of experience in Machine Learning or AI Engineering.Strong proficiency in Python.Hands on experience with PyTorch, scikit learn, and Hugging Face.Experience building supervised learning models using real world datasets.Strong understanding of data preprocessing, feature engineering, and handling class imbalance.Experience building and optimizing production RAG systems.Experience designing evaluation frameworks and model benchmarking.Ability to independently collect, label, clean, and prepare training datasets.Strong analytical and problem solving skills. Nice to Have LLM fine tuningRLHF (Reinforcement Learning from Human Feedback)Amazon SageMakerExperience in fintech, insurtech, legal tech, or other regulated industriesInference cost optimizationSelf hosted LLM deployment Tech Stack Languages & Frameworks: Python, PyTorch, scikit learn, Hugging Face AI & ML: Large Language Models, RAG, Supervised Learning, Model Evaluation Infrastructure: DigitalOcean, Cloudflare, GitHub Actions Database: PostgreSQL, pgvector If you're passionate about building production grade AI systems, improving model quality, and solving real world machine learning challenges, we'd love to hear from you.