Machine Learning Engineer Resume Keywords (Tools and Platforms Keywords + ATS Examples)
MLE platforms are rapidly standardizing. Show alignment with MLflow, feature stores, and cloud ML services you actually used.
Cloud ML services
Where models train and deploy.
- SageMaker
- Vertex AI
- Azure ML
- Databricks
- Ray
- Horovod
- GCP TPUs
- AWS Trainium
- Batch
- Endpoints
Weak: SageMaker
Strong: Training jobs with spot instances and checkpointing.
Weak: Vertex AI
Strong: Model registry and endpoint traffic splitting.
Feature stores & offline/online consistency
Modern ML systems.
- Feast
- Tecton
- Databricks Feature Store
- SageMaker Feature Store
- Redis
- DynamoDB
- point-in-time joins
- TTL
- backfill jobs
Weak: Feast
Strong: Online store for low-latency features with monitored freshness.
Weak: Tecton
Strong: Materialized features with validation on null rates.
Orchestration & data pipelines
How training runs.
- Airflow
- Prefect
- Dagster
- Luigi
- Spark
- dbt
- Kafka
- Flink
- Beam
- Dataflow
Weak: Airflow
Strong: DAGs with SLAs and alerting on upstream delays.
Weak: Spark
Strong: Large-scale feature backfills with partition pruning.
Model registries & experiment tracking
Governance.
- MLflow
- Weights & Biases
- Neptune
- Comet
- Kubeflow Pipelines
- model registry
- artifact signing
- promotion
- approval workflows
Weak: MLflow
Strong: Model stages with promotion gates on offline metrics.
Weak: W&B
Strong: Sweep experiments tracked for reproducibility.
Serving & GPU infrastructure
Inference path.
- TorchServe
- TensorRT
- Triton
- ONNX Runtime
- Knative
- KFServing
- KServe
- NVIDIA GPU
- CUDA
- autoscaling
Weak: Triton
Strong: Dynamic batching configuration for latency/cost tradeoff.
Weak: TensorRT
Strong: FP16 conversion with accuracy regression checks.
Where to use these keywords (ATS + readability)
Skills
Separate Training / Features / Serving / Ops in your skills section.
Experience bullets
Each platform bullet: workload + metric (latency, cost, quality).
Skills
Feature store name when JD mentions it explicitly.
Summary
Cloud ML focus helps if employer is AWS/GCP/Azure heavy.
Experience bullets
Registry/tools without governance story feel weak—add promotion criteria.
Common mistakes
- SageMaker listed without training or endpoint story.
- Feature store buzzword with no leakage or freshness detail.
- Experiment tracking tools without reproducibility practices.
- Serving tools without latency/cost tradeoffs.