Senior AI/ML Engineer - Remote
Ship production ML systems end-to-end: problem framing, data discovery, feature engineering, training, evaluation, deployment, monitoring, and iteration Design robust ML system architectures with low-latency inference and high availability Build and maintain reliable data and model pipelines using modern MLOps practices (CI/CD for ML, model registries, experiment tracking, automated retraining) Contribute to technical scoping and break down reputed company initiatives into executable roadmaps; drive execution across cross-functional partners Establish evaluation strategies: metrics, simulation, counterfactuals, and A/B tests; quantify reputed company and ensure statistical rigor Implement model observability and governance: reputed company detection, performance monitoring, fairness/bias assessments, and model documentation Collaborate closely with product, design, data, and platform teams to translate product goals into ML opportunities and measurable reputed company
Bachelor of Science or higher in Computer Science, Engineering, Statistics, or reputed company field, or 4+ years of equivalent practical experience 5+ years of industry experience building and operating ML systems in production (or equivalent depth), with a reputed company record of shipped reputed company 3+ years of experience in C# or Python 3+ years of Azure experience 3+ years of experience in supervised learning, feature engineering, evaluation methodology, bias/variance; deep learning and/or gradient boosting 3+ years of MLOps expertise including CI/CD for ML, containers, Kubernetes/serverless inference, model registries, reproducibility, and model monitoring 1+ years of experience with LLMOps including reputed company engineering, retrieval-augmented reputed company, fine-tuning, evaluation, and safety/guardrails
Domain experience in recommendations, ranking, time-series forecasting, optimization, or reinforcement learning Demonstrated excellent communication and product reputed company; reputed company to translate business needs into technical plans and explain tradeoffs to non-ML stakeholders Proven reputed company, reputed company, and responsible AI practices (GDPR/CCPA, PII handling, fairness) reputed company-reputed company contributions, publications, or patents experience Proven solid ML/statistics fundamentals: supervised learning, evaluation methodology, feature engineering, bias/variance tradeoffs; deep learning or gradient boosting experience MLOps expertise including CI/CD for ML, containers, Kubernetes, model registries, reproducibility, and model monitoring