MLOps Engineer Resume Keywords

What You Need to Know

MLOps engineers bridge the gap between data science and production systems, ensuring ML models don't just work in notebooks but deliver value at scale. Model deployment seems straightforward until you need to serve predictions with sub-100ms latency to millions of users. Model monitoring is essential because data drift silently degrades accuracy—a model trained on 2023 data might perform poorly on 2025 patterns. Feature stores solve the problem of training-serving skew by ensuring models use the same features in production as they did during training. ML pipelines automate retraining, but knowing when to retrain requires monitoring dozens of metrics. Model versioning becomes complex when you're A/B testing five model variants simultaneously while maintaining rollback capability. Experiment tracking with tools like MLflow or Weights & Biases helps teams remember which hyperparameters produced which results six months ago. The MLOps field emerged because data scientists are great at building models but often lack the engineering skills to deploy them reliably at scale. Deploying ML models to production is fundamentally different from deploying traditional software. Models are probabilistic, not deterministic—the same input might produce different outputs due to randomness. Models degrade over time as data patterns change, requiring monitoring and retraining. Model files can be gigabytes in size, making deployment and loading slow. Inference latency matters enormously for user-facing applications. And models often depend on specific library versions, creating dependency management nightmares. MLOps engineers solve these problems by building robust infrastructure for the ML lifecycle. Model serving infrastructure needs to handle prediction requests efficiently. Batch prediction works for some use cases—processing millions of predictions overnight—but requires different infrastructure than real-time serving. Real-time APIs need to respond in milliseconds, which requires optimized model formats, efficient preprocessing, and careful resource management. Some teams use specialized serving frameworks like TensorFlow Serving or TorchServe, while others build custom solutions. GPU utilization matters for deep learning models, but GPUs are expensive and require batching requests for efficiency. Feature stores provide a centralized repository for features used in ML models. Without feature stores, data scientists might compute features differently during training versus production, causing training-serving skew that degrades model performance. Feature stores ensure consistency, enable feature reuse across models, and provide point-in-time correct features for training historical data. Building feature stores requires understanding both batch processing for training and low-latency serving for predictions. Systems like Feast, Tecton, or custom-built solutions each have trade-offs. Model monitoring is critical because ML models fail silently. Unlike traditional software that crashes with error messages, degraded models just make worse predictions. Data drift occurs when input distributions change—a model trained on summer data might perform poorly in winter. Concept drift occurs when the relationship between inputs and outputs changes—user behavior shifts over time. Monitoring requires tracking input distributions, prediction distributions, and business metrics. Setting up effective monitoring without drowning in metrics requires understanding which indicators actually matter. Automated retraining addresses model degradation but requires careful implementation. Triggers might be time-based (retrain monthly), performance-based (retrain when accuracy drops), or data-based (retrain when sufficient new data arrives). But retraining isn't just running the training script again—you need to validate new models, compare them to production models, and decide whether to deploy. Automated deployment of retrained models requires confidence that the retraining process is robust and monitoring will catch any issues. ML pipelines orchestrate the steps from data collection through deployment. Pipelines might include data validation, feature engineering, training, evaluation, and deployment. Tools like Kubeflow, MLflow, or Airflow help build pipelines, but choosing the right abstraction level is challenging. Too much abstraction hides important details; too little requires reimplementing common patterns. Pipeline versioning ensures reproducibility—you need to recreate the exact model from six months ago for debugging or compliance. Experiment tracking becomes essential as teams run hundreds of training experiments. Which hyperparameters produced the best results? Which dataset version was used? What was the random seed? Tools like MLflow, Weights & Biases, or Neptune track experiments, but they require discipline to use consistently. Without tracking, teams waste time rediscovering what works or can't reproduce past results. Model versioning and registry provide central repositories for trained models. As teams create dozens of model versions, registries track which version is in production, which is being A/B tested, and which failed validation. Registries also store model metadata, performance metrics, and deployment history. Building robust versioning requires understanding semantic versioning for models—when is a change big enough to warrant a new major version? Model optimization reduces latency and resource usage. Quantization reduces model precision from 32-bit to 8-bit, shrinking models and speeding inference with minimal accuracy loss. Pruning removes unimportant weights. Knowledge distillation trains smaller models to mimic larger ones. Model compilation tools like TensorRT or ONNX optimize models for specific hardware. But optimization requires careful validation because aggressive optimization can degrade accuracy unexpectedly. A/B testing ML models requires infrastructure to route traffic to different model versions and measure business impact. Statistical significance testing ensures observed differences aren't just noise. But A/B testing models is trickier than testing UI changes because model performance varies across user segments. A new model might improve average accuracy but harm accuracy for important customer segments. Analyzing results requires both statistical rigor and business understanding. Compliance and governance matter increasingly as ML is used in regulated industries. Model explainability helps audit decisions—why did the model deny a loan application? Bias detection identifies when models discriminate against protected groups. Model cards document model purpose, limitations, and performance across demographics. Audit trails track who trained models, what data was used, and when models were deployed. Building governance into MLOps processes requires understanding both technical mechanisms and regulatory requirements. Working as an MLOps engineer requires understanding both machine learning and production engineering. You need to speak the language of data scientists while thinking about reliability, scalability, and maintainability like a software engineer. The field is new and evolving rapidly, with new tools and practices emerging constantly. But the core challenges—deploying models reliably, monitoring their performance, and iterating quickly—remain consistent. Success requires both breadth of knowledge across many tools and depth in understanding ML systems' unique challenges.