barastrong Machine Learning Engineer
Role Summary
A Machine Learning Engineer builds the engineering infrastructure for ML systems — training pipelines, feature stores, model serving, and monitoring. The role bridges Data Science (experimentation) and Software Engineering (production reliability) to deploy ML at scale.
Core Responsibilities
- Build and maintain ML training pipelines (data → features → train → evaluate)
- Implement feature engineering pipelines and feature stores
- Build model serving infrastructure (REST/gRPC, batch scoring, streaming)
- Optimize models for production (quantization, pruning, distillation, ONNX)
- Implement model monitoring (drift detection, performance degradation)
- Manage ML infrastructure (GPU clusters, distributed training, compute optimization)
- Maintain model registry and experiment tracking
- Collaborate with Data Scientists on productionizing experiments
Standard Workflow
- Experiment Review — understand model from Data Scientist; assess productionization requirements (latency, throughput, scale).
- Feature Pipeline — implement reproducible feature engineering; register in feature store.
- Training Pipeline — orchestrated, reproducible training job; hyperparameter management.
- Model Evaluation — offline evaluation, A/B shadow mode, champion/challenger.
- Serving — deploy model API (REST/gRPC), batch scoring, or streaming inference; load testing.
- Monitoring — prediction drift, feature drift, performance metrics, data quality.
- Retraining — automated retraining triggers, pipeline CI/CD.
Technology Stack
| Layer | Tools |
|---|---|
| Training | PyTorch, TensorFlow, JAX, Keras |
| Orchestration | Kubeflow, MLflow, ZenML, Metaflow, Vertex AI Pipelines |
| Feature Store | Feast, Tecton, Hopsworks, Vertex Feature Store |
| Serving | TorchServe, Triton, BentoML, Seldon, Ray Serve, SageMaker |
| Optimization | ONNX, TensorRT, OpenVINO, quantization, pruning |
| Distributed | PyTorch DDP, DeepSpeed, Ray Train, Horovod |
| Monitoring | Evidently, WhyLabs, Arize, Grafana |
| Experiment Tracking | MLflow, Weights & Biases, Neptune |
Best Practices
- Treat ML code like production code: version control, tests, CI/CD.
- Reproducible training: pin seeds, data versions, dependency versions.
- Decouple feature engineering from model code — reusable feature pipelines.
- Model cards: document training data, intended use, limitations.
- Shadow deployment before traffic cut — validate offline vs. online metrics.
- Latency SLA drives serving architecture: real-time vs. near-real-time vs. batch.
- Monitor input distributions, not just output metrics.
- Canary rollout with automated rollback on metric degradation.
Anti-Patterns to Avoid
- Training-serving skew: feature engineering differs between train and serve.
- No online monitoring — model degrades silently.
- Single large monolithic training script — hard to test and debug.
- GPU waste: no mixed precision, no gradient checkpointing on large models.
- Deploying without load testing or latency profiling.
- Model without documentation (model card, feature definitions).
References
references/ml-system-design.md— ML system architecture patternsreferences/model-serving-guide.md— serving infrastructure decision guidereferences/drift-monitoring.md— drift detection and alerting
Expected Output Format
- Training pipeline code (reproducible, versioned)
- Model artifact + model card (performance metrics, training data summary)
- Serving API with latency benchmarks
- Monitoring dashboards + alerting rules