Independently implements Apache Kafka tasks in Kubeflow/MLflow. Understands internals and optimizes performance. Writes tests using model monitoring.

Independently configures ArgoCD Applications for ML platform components including model registries, experiment tracking, and serving infrastructure. Writes ApplicationSets for multi-environment model deployment pipelines with promotion workflows. Implements custom health checks for ML-specific resources like InferenceServices, TrainingJobs, and feature store deployments.

Configures feature store platforms like Feast or Tecton for online and offline serving. Builds automated pipelines for feature computation, validation, and backfilling. Monitors feature drift and data quality metrics in production.

Independently creates multi-stage GitLab pipelines for ML workflows (data validation, training, evaluation, registry push). Configures GPU runners, DVC integration, and model registry artifacts. Automates experiment tracking and model promotion with pipeline triggers and API calls.

Develops gRPC services for ML inference: defines proto schemas for prediction requests with typed tensors, implements streaming RPC for sequence processing. Configures gRPC clients with retry policies and deadlines for model serving calls, optimizes large tensor serialization via Protobuf.

Independently configures and manages Helm. Writes IaC for typical tasks. Understands networking and security basics.

Independently configures and manages Kubernetes Advanced. Writes IaC for typical tasks. Understands networking and security basics.

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.

Configures MLflow Tracking Server for the team: remote backend store on PostgreSQL, artifact store in S3. Implements automatic logging via mlflow.autolog for PyTorch/TensorFlow/XGBoost, configures custom metrics and tags for experiment filtering. Integrates MLflow into training pipelines for result reproducibility.

Deploys models to production via specialized serving platforms: TensorFlow Serving for TF models, Triton Inference Server for multi-framework serving. Configures BentoML for packaging models with dependencies, implements batch inference via Spark/Ray, and configures model versioning for seamless production model updates.

Independently implements Python Web Framework tasks in Kubeflow/MLflow. Understands internals and optimizes performance. Writes tests using model monitoring.

Independently implements S3 / Object Storage tasks in Kubeflow/MLflow. Understands internals and optimizes performance. Writes tests using model monitoring.

Independently implements Task Queue tasks in Kubeflow/MLflow. Understands internals and optimizes performance. Writes tests using model monitoring.

Writes Terraform modules for deploying ML infrastructure: S3 buckets for model artifacts, ECR for ML images, SageMaker endpoints for inference. Manages state via remote backend, uses workspaces for separating staging/production ML environments, and applies variables for parameterizing GPU instances.

Configures custom metrics for ML pipelines including training throughput, feature store freshness, and inference SLOs. Builds alerting rules for model degradation and data pipeline failures. Analyzes production incidents affecting model serving.

Configures ML model monitoring in production: collecting prediction logs, calculating quality metrics on new data, detecting data drift via statistical tests (KS-test, PSI). Integrates Evidently AI or Whylogs for automated distribution monitoring, configures Grafana dashboards with model metrics, and sets up accuracy/F1 degradation alerts.

Conducts load testing of inference services using Locust or k6: measuring prediction latency at different RPS, verifying stability under batch requests. Analyzes test results — p50/p95/p99 latency, throughput, error rate — and identifies performance degradation with increasing input data size.

Integrates security scanning into CI/CD for ML images: configuring Trivy/Grype in the inference container build pipeline, filtering false positives for scientific Python packages. Configures vulnerability admission policies considering ML dependency specifics (numpy, scipy, CUDA) and blocks deployment of images with critical CVEs.

Configures experiment tracking for ML projects: organizing experiments by tasks, automatic logging via callbacks in PyTorch Lightning/Keras. Implements model comparison across multiple metrics, training visualization via W&B plots, dataset and config tracking for full reproducibility of every experiment in the team.