Machine Learning Engineer

Machine Learning Engineer

Remote

Exp: 12+yrs

Required Skills:

Hands-on engineering resource responsible for converting ML and analytical strategies into repeatable feature-engineering pipelines, ML workflows, model lifecycle processes, and operational integrations in support of Cisco Learning’s opportunity-assessment solution.

Primary Focus

ML Engineering + Feature Pipelines + Model Operationalization + MLOps + Data/ML Integration

Required Skills

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

1. ML Pipeline + Feature Engineering Development

The strongest screen should be someone who has actually built ML and feature-engineering pipelines, not someone whose experience stops at ETL or general data engineering. The project scope specifically includes automated ingestion, feature engineering, ML pipeline infrastructure, monitoring, training/retraining, and versioning.

2. Python + Hands-On Machine Learning Engineering

Strong hands-on Python development paired with genuine ML engineering experience. Candidates should understand how features, models, training, inference, and operational workflows connect.

3. MLOps / Model Lifecycle Management

Experience operationalizing ML through model registries, versioning, experiment tracking, monitoring, automated testing, reproducibility, and retraining workflows.

***** Prioritize:

ML Engineering → Feature Pipelines → Python → Model Operationalization/MLOps → SQL/Data → Cloud ML → Integration

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