Skill Profile

ML Pipelines

Kubeflow, Vertex AI, SageMaker Pipelines, reproducibility, training automation

Machine Learning & AI MLOps

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 9 optional

Domain

Machine Learning & AI

Group

MLOps

Last updated

3/17/2026

How to Use

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
Computer Vision Engineer		Understands ML pipeline stages for computer vision: data loading, augmentation, training, and evaluation. Runs existing pipelines and tracks experiment metrics using MLflow or Weights & Biases.
Data Scientist	Required	Creates basic ML pipelines via scikit-learn Pipeline: preprocessing, feature engineering, model training. Understands pipeline importance for reproducibility and data leakage prevention. Saves pipelines as single artifacts for deployment.
LLM Engineer		Knows ML pipeline basics: data loading, preprocessing, training, evaluation. Builds simple pipelines for LLM fine-tuning data preparation using Hugging Face Datasets.
ML Engineer	Required	Understands ML pipeline concept: data → features → training → evaluation → deployment. Writes simple pipeline scripts. Uses Airflow DAG for basic ML workflow.
MLOps Engineer		Understands ML pipeline concepts: data validation, model training, and deployment stages. Configures pipeline runners (Airflow, Kubeflow) and monitors pipeline health metrics.
NLP Engineer	Required	Knows ML pipeline basics for NLP: data collection, preprocessing, feature extraction, training, evaluation. Uses scikit-learn Pipeline and spaCy for building simple NLP pipelines.

Role	Required	Description
Computer Vision Engineer		Builds end-to-end ML pipelines for vision models: dataset versioning, distributed training, hyperparameter tuning, and model registry integration. Implements data quality checks for image datasets.
Data Scientist	Required	Designs production ML pipelines using Airflow, Prefect, or Dagster. Automates the full ML cycle: data ingestion, validation, feature engineering, training, evaluation, deployment. Configures scheduling and retry logic.
LLM Engineer		Independently builds production ML pipelines for LLM: data ingestion, cleaning, tokenization, training, evaluation. Uses Airflow or Prefect for orchestration, ensures idempotency.
ML Engineer	Required	Designs ML pipelines with Kubeflow/Airflow. Configures parameterized pipelines for different models. Automates retraining with data quality checks. Implements pipeline testing.
MLOps Engineer		Implements automated ML pipelines with feature stores, model versioning, and A/B testing infrastructure. Builds CI/CD for model training with automated retraining triggers on data drift detection.
NLP Engineer	Required	Independently designs ML pipelines for NLP tasks: data versioning, text feature engineering, hyperparameter tuning, model selection. Automates via Airflow or Prefect.

Role	Required	Description
Computer Vision Engineer	Required	Designs scalable ML pipeline architectures for large-scale vision systems: multi-GPU training orchestration, model distillation workflows, and production inference pipelines with latency optimization.
Data Scientist	Required	Designs scalable ML pipelines for enterprise: Kubeflow, Vertex AI Pipelines, SageMaker Pipelines. Implements continuous training, automated model promotion. Optimizes pipeline performance through caching, parallel execution, and incremental processing.
LLM Engineer		Designs complex ML pipelines for LLM platforms: multi-stage data processing, continuous training, automated retraining triggers. Optimizes pipeline throughput and reliability.
ML Engineer	Required	Designs ML pipeline architecture. Optimizes pipeline execution (caching, parallel steps). Configures CI/CD for pipeline deployment. Ensures reproducibility.
MLOps Engineer	Required	Designs enterprise ML platform with pipeline orchestration, model governance, and reproducibility guarantees. Implements canary deployments for models, automated rollback on performance degradation, and cost-optimized training infrastructure.
NLP Engineer	Required	Designs production ML pipelines for NLP systems. Implements CI/CD for models, automatic retraining on drift detection, A/B testing of NLP models with automatic promotion.

Role	Required	Description
Computer Vision Engineer	Required	Defines ML Pipelines strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Data Scientist	Required	Defines ML pipeline infrastructure strategy for the data science team. Establishes pipeline development, testing, and monitoring standards. Coordinates unification of pipeline approaches across projects and teams.
LLM Engineer		Defines ML pipeline standards for the LLM team. Establishes guidelines for pipeline architecture, monitoring, error handling. Coordinates pipeline infrastructure for training and inference.
ML Engineer	Required	Defines ML pipeline strategy. Standardizes pipeline components. Designs pipeline templating for faster development.
MLOps Engineer	Required	Defines the ML Pipelines strategy at team/product level. Establishes standards and best practices. Conducts reviews.
NLP Engineer	Required	Defines ML pipeline standards for the NLP team. Establishes MLOps best practices, defines model lifecycle management processes, and ensures reproducibility of all NLP experiments.

Role	Required	Description
Computer Vision Engineer	Required	Defines ML Pipelines strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.
Data Scientist	Required	Shapes ML orchestration platform strategy at organizational level. Defines enterprise requirements: scalability, governance, cost management. Designs unified ML pipeline platform for all data science teams.
LLM Engineer		Shapes enterprise ML pipeline platform. Defines approaches to unified pipeline frameworks, cross-team pipeline components, and SLA and cost management for data/training/serving pipelines.
ML Engineer	Required	Defines ML pipeline platform strategy. Evaluates pipeline orchestrators. Designs enterprise ML pipeline architecture.
MLOps Engineer	Required	Defines the ML Pipelines strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.
NLP Engineer	Required	Shapes enterprise MLOps strategy for the NLP platform. Defines ML pipeline standards, model governance, and infrastructure for scaling NLP ML operations at organizational level.

Junior 6 requirements

Computer Vision Engineer

Understands ML pipeline stages for computer vision: data loading, augmentation, training, and evaluation. Runs existing pipelines and tracks experiment metrics using MLflow or Weights & Biases.
Data Scientist
Required

Creates basic ML pipelines via scikit-learn Pipeline: preprocessing, feature engineering, model training. Understands pipeline importance for reproducibility and data leakage prevention. Saves pipelines as single artifacts for deployment.
LLM Engineer

Knows ML pipeline basics: data loading, preprocessing, training, evaluation. Builds simple pipelines for LLM fine-tuning data preparation using Hugging Face Datasets.

Middle 6 requirements

Computer Vision Engineer

Builds end-to-end ML pipelines for vision models: dataset versioning, distributed training, hyperparameter tuning, and model registry integration. Implements data quality checks for image datasets.
Data Scientist
Required

Designs production ML pipelines using Airflow, Prefect, or Dagster. Automates the full ML cycle: data ingestion, validation, feature engineering, training, evaluation, deployment. Configures scheduling and retry logic.
LLM Engineer

Independently builds production ML pipelines for LLM: data ingestion, cleaning, tokenization, training, evaluation. Uses Airflow or Prefect for orchestration, ensures idempotency.

Senior 6 requirements

Computer Vision Engineer
Required

Designs scalable ML pipeline architectures for large-scale vision systems: multi-GPU training orchestration, model distillation workflows, and production inference pipelines with latency optimization.
Data Scientist
Required

Designs scalable ML pipelines for enterprise: Kubeflow, Vertex AI Pipelines, SageMaker Pipelines. Implements continuous training, automated model promotion. Optimizes pipeline performance through caching, parallel execution, and incremental processing.
LLM Engineer

Designs complex ML pipelines for LLM platforms: multi-stage data processing, continuous training, automated retraining triggers. Optimizes pipeline throughput and reliability.

Lead / Staff 6 requirements

Computer Vision Engineer
Required

Defines ML Pipelines strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Data Scientist
Required

Defines ML pipeline infrastructure strategy for the data science team. Establishes pipeline development, testing, and monitoring standards. Coordinates unification of pipeline approaches across projects and teams.
LLM Engineer

Defines ML pipeline standards for the LLM team. Establishes guidelines for pipeline architecture, monitoring, error handling. Coordinates pipeline infrastructure for training and inference.

Principal 6 requirements

Computer Vision Engineer
Required

Defines ML Pipelines strategy at the organizational level. Establishes enterprise approaches. Mentors leads and architects.
Data Scientist
Required

Shapes ML orchestration platform strategy at organizational level. Defines enterprise requirements: scalability, governance, cost management. Designs unified ML pipeline platform for all data science teams.
LLM Engineer

Shapes enterprise ML pipeline platform. Defines approaches to unified pipeline frameworks, cross-team pipeline components, and SLA and cost management for data/training/serving pipelines.

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