Machine Learning Engineer

Description

Summary: Design, develop, and deploy machine learning models and AI systems to solve product and business problems, collaborating with cross-functional teams to ensure model reliability, scalability, and observability. Highlights: 1. Develop and implement ML models from research prototyping to production. 2. Collaborate with product managers, engineers, and researchers on ML solutions. 3. Stay current with ML research and evaluate new algorithms and tooling. **Job Title \- Machine Learning Engineer** **Summary** Design, develop, and deploy machine learning models and AI systems to solve product and business problems. Work with cross\-functional teams to productionize models, maintain data pipelines, and ensure model reliability, scalability, and observability. **Key Responsibilities** * Develop and implement ML models (supervised, unsupervised, deep learning) from research prototyping to production. * Design, build, and maintain data pipelines and feature stores for training and inference. * Collaborate with product managers, engineers, and researchers to define requirements and translate business needs into ML solutions. * Deploy models to production (cloud or on\-prem), integrating with APIs or microservices. * Optimize model performance, latency, and cost (quantization, distillation, pruning, batching). * Implement monitoring, logging, and alerting for model performance and data drift; perform model retraining as needed. * Ensure reproducible experiments, version control for code and models, and maintain model documentation. * Conduct error analysis, A/B tests, and performance evaluations; iterate on models based on metrics. * Stay current with ML research and evaluate new algorithms, tooling, and frameworks. * Mentor junior engineers and help establish best practices for ML lifecycle (MLOps). **Required Qualifications** * BS or MS in Computer Science, Electrical Engineering, Statistics, or related field (or equivalent experience). * 3\+ years experience building and deploying ML systems or models in production. * Strong programming skills in Python; proficiency with ML libraries (PyTorch, TensorFlow, scikit\-learn). * Experience with data engineering tools (SQL, Pandas, Spark). * Familiarity with cloud platforms and services (AWS/GCP/Azure), containerization (Docker), and orchestration (Kubernetes). * Knowledge of ML model evaluation, cross\-validation, metrics, and common pitfalls (data leakage, overfitting). * Experience with CI/CD, unit testing, and code review processes. * Strong communication and collaboration skills. **Preferred Qualifications** * Advanced degree (MS/PhD) in ML/AI/Statistics or related field. * Experience with large language models, transformer architectures, or generative models. * Familiarity with MLOps tools: MLflow, TFX, Kubeflow, Seldon, BentoML. * Knowledge of model explainability, fairness, and privacy\-preserving ML techniques. * Experience with real\-time inference systems and low\-latency requirements. * Background in software engineering best practices and scalable system design. **Skills \& Tools** * Languages: Python (required), Java/Go/Scala (nice\-to\-have) * Frameworks: PyTorch, TensorFlow, Keras, scikit\-learn * Data: SQL, Pandas, Spark * DevOps: Docker, Kubernetes, Terraform, CI/CD (GitHub Actions, Jenkins) * Cloud: AWS, GCP, or Azure * MLOps: MLflow, TFX, Kubeflow, Seldon, Feast Pay: QAR12,744\.50 \- QAR20,834\.12 per month Work Location: In person