Matching/Machine Learning Engineer/

Match Report

Bas van Driel — Machine Learning Engineer at Unknown

Global Score

41%

Detailed Score

45%

Final Score

30%

Requirement Breakdown36

Kubernetes ML workflows

must have
strong match
72%
Matched with: Kubernetes

Ervaring met Kubernetes-gebaseerde ML-workflows

Kubernetes ML workflows

must have
strong match
72%
Matched with: Kubernetes

Ervaring met Kubernetes-gebaseerde ML-workflows

ML Engineer experience

must have
good match
60%
Matched with: AI

Minimaal 4 jaar ervaring als ML Engineer

ML Engineer experience

must have
good match
60%
Matched with: AI

Minimaal 4 jaar ervaring als ML Engineer

Python programming

must have
good match
59%
Matched with: Python

Sterke Python-kennis (PyTorch of TensorFlow)

Python programming

must have
good match
59%
Matched with: Python

Sterke Python-kennis (PyTorch of TensorFlow)

Time series analysis

must have
good match
56%
Matched with: Data Science

Ervaring met time series analyse

Time series analysis

must have
good match
56%
Matched with: Data Science

Ervaring met time series analyse

PyTorch framework

must have
no match
0%

Sterke Python-kennis (PyTorch of TensorFlow)

TensorFlow framework

must have
no match
0%

Sterke Python-kennis (PyTorch of TensorFlow)

Feature engineering for sensor data

must have
no match
0%

Bekendheid met feature engineering voor sensordata

Academic degree MSc/PhD

must have
no match
0%

Academisch denkniveau (MSc of PhD in relevante richting)

MLOps practices

must have
no match
0%

Kennis van MLOps (model registry, deployment, monitoring)

TensorFlow framework

must have
no match
0%

Sterke Python-kennis (PyTorch of TensorFlow)

Academic degree MSc/PhD

must have
no match
0%

Academisch denkniveau (MSc of PhD in relevante richting)

MLOps practices

must have
no match
0%

Kennis van MLOps (model registry, deployment, monitoring)

PyTorch framework

must have
no match
0%

Sterke Python-kennis (PyTorch of TensorFlow)

Feature engineering for sensor data

must have
no match
0%

Bekendheid met feature engineering voor sensordata

Databricks platform

nice to have
strong match
77%
Matched with: Azure Databricks

Ervaring met Databricks of Kubeflow

Databricks platform

nice to have
strong match
77%
Matched with: Azure Databricks

Ervaring met Databricks of Kubeflow

Kubeflow ML platform

nice to have
good match
60%
Matched with: Kubernetes

Ervaring met Databricks of Kubeflow

Kubeflow ML platform

nice to have
good match
60%
Matched with: Kubernetes

Ervaring met Databricks of Kubeflow

OPC-UA protocol

nice to have
no match
0%

Bekendheid met industriële dataprotocollen (OPC-UA, MQTT)

MQTT protocol

nice to have
no match
0%

Bekendheid met industriële dataprotocollen (OPC-UA, MQTT)

Edge AI deployment

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

Digital twins knowledge

nice to have
no match
0%

Kennis van digital twins of process simulation

ONNX model format

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

TensorRT optimization

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

OPC-UA protocol

nice to have
no match
0%

Bekendheid met industriële dataprotocollen (OPC-UA, MQTT)

MQTT protocol

nice to have
no match
0%

Bekendheid met industriële dataprotocollen (OPC-UA, MQTT)

Anomaly detection algorithms

nice to have
no match
0%

Ervaring met anomaly detection algoritmen

TensorRT optimization

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

Edge AI deployment

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

ONNX model format

nice to have
no match
0%

Ervaring met edge AI-deployment (TensorRT, ONNX)

Digital twins knowledge

nice to have
no match
0%

Kennis van digital twins of process simulation

Anomaly detection algorithms

nice to have
no match
0%

Ervaring met anomaly detection algoritmen