Machine Learning Engineer

**About The Role** The Machine Learning Engineer designs, trains, and ships production ML systems that run at real scale \- moving beyond notebooks into deployed, monitored, continuously improving models. The role sits at the intersection of research and infrastructure, working closely with data scientists, backend engineers, and product teams to turn experimental ideas into reliable systems with measurable business impact. **Key Responsibilities** * Design, train, and evaluate ML models for production use cases \- including recommendation, ranking, NLP, and forecasting problems * Build end\-to\-end training and inference pipelines in Python, leveraging PyTorch or TensorFlow alongside distributed training frameworks * Own model deployment on cloud platforms (AWS, GCP, or Azure) including containerization, CI/CD for ML, and staged rollout strategies * Develop offline and online evaluation frameworks, including A/B testing, shadow deployments, and statistical significance analysis * Monitor production models for data drift and performance degradation; build automated retraining and alerting pipelines * Collaborate with data engineering teams to design robust feature stores and ensure data quality across training and serving paths * Contribute to engineering standards through code reviews, documentation, and mentorship of junior engineers **What We Are Looking For** * 3–6 years of experience in machine learning engineering, applied ML, or a closely related field * Strong Python engineering skills and deep hands\-on proficiency with PyTorch, TensorFlow, or JAX * Experience deploying and operating models in production on AWS, GCP, or Azure * Proficiency with ML tooling: experiment tracking (MLflow, Weights \& Biases), orchestration (Airflow, Kubeflow), and containerization (Docker, Kubernetes) * Solid ML fundamentals: model selection, evaluation metrics, regularization, and distributed training strategies * MS or PhD in Computer Science, Statistics, or a related quantitative field, or equivalent industry experience * Bonus: experience with LLM fine\-tuning, large\-scale distributed training, real\-time inference optimization (TensorRT, ONNX), or open\-source contributions