Machine Learning Engineer

Required Skills

Languages & Data

ML Engineering

MLOps / Model Lifecycle

Data / Integration

Preferred Skills

Strong candidate signals

Languages & Data

• Strong Python
• SQL
• Pandas / NumPy or comparable data-processing libraries
• Structured and unstructured data processing
• Software-development and version-control practices

ML Engineering

• Feature engineering
• ML pipeline development
• Model training and evaluation
• Model inference
• Data preprocessing/transformation
• Scikit-learn or comparable ML frameworks
• Production-oriented ML development

MLOps / Model Lifecycle

• Model registries
• Model versioning
• Experiment tracking
• Model monitoring
• Automated testing
• Retraining workflows
• Reproducible ML pipelines

Data / Integration

• Data ingestion/access pipelines
• Cloud-based ML/data environments
• APIs and/or downstream integrations
• Enterprise data environment

Preferred Skills

• MLflow or comparable ML lifecycle tooling
• Feature Store experience
• Containerization
• Cloud ML platforms
• API/integration development
• NLP/text-processing pipelines
• Document/vector ingestion
• Model inference and monitoring
• Automated ML testing/retraining
• Enterprise data-platform experience
• Production-oriented ML solutions
• Previous Cisco experience with the appropriate ML engineering skill set

Strong candidate signals

• Can provide concrete examples of building feature pipelines and ML workflows
• Has moved ML models beyond notebooks/experimentation into repeatable execution processes
• Strong Python engineering experience
• Understands model registry/versioning/monitoring concepts
• Comfortable partnering closely with a Data Scientist
• Has worked in cloud-based enterprise ML environments
• Has Cisco and/or large-enterprise experience in addition to the core ML engineering skill set Watch-outs
• Pure Data Engineer / ETL profile
• GenAI/LLM background without traditional ML engineering depth
• Data Scientist who primarily builds models but has little experience operationalizing them
• MLOps/DevOps candidate without meaningful understanding of ML features, training, inference, and model lifecycle
• The current KCS JD specifically says this is not a pure Data Engineering/ETL position and that GenAI experience can be complementary but should not replace core hands-on ML engineering capability.