AI Engineer

Role summary: Design, build, and deploy AI/ML solutions that turn data into reliable, scalable products. Partner with cross-functional teams to deliver models, services, and pipelines that meet performance, quality, and compliance requirements. Core responsibilities - Develop and optimize machine learning models (training, evaluation, tuning, and validation). - Build production-grade inference services and integrate models into applications via APIs. - Create and maintain data pipelines for feature engineering, labeling, and model retraining. - Implement MLOps practices: CI/CD for ML, model versioning, monitoring, and drift detection. - Collaborate with product, engineering, and stakeholders to translate requirements into technical solutions. - Ensure responsible AI practices, including privacy, security, bias testing, and documentation. Required skills - Programming: Python; strong software engineering fundamentals (testing, code review, design patterns). - ML frameworks: PyTorch and/or TensorFlow; experience with classical ML and deep learning. - Data: SQL; data wrangling; feature engineering; experiment tracking. - Deployment: REST/gRPC APIs; Docker; cloud platforms (AWS/Azure/GCP) and scalable compute. - MLOps: MLflow/Kubeflow/SageMaker or similar; monitoring and observability. - Communication: clear documentation and ability to explain model behavior and trade-offs. Success measures - Models meet accuracy/latency/cost targets and are stable in production. - Automated pipelines reduce time-to-deploy and support repeatable ...