Key skills
AWSPySparkPythonSparkMachine LearningMLMLOpsMLflowS3IAMCI/CD
About this role
Role Overview
- Design, build, and productionize ML pipelines for a Trust Scoring platform
- Productionize PoC ML models into reproducible, governed pipelines
- Implement deterministic preprocessing for train vs serve parity
- Develop batch and near‑real‑time inference workflows
- Generate explainability artifacts (reason codes, score attribution)
- Implement and maintain MLflow (experiments, model registry)
- CI/CD pipelines for ML
- Champion/Challenger model frameworks
- Enable controlled rollouts (shadow, advisory, active scoring)
- Versioned feature and model deployments
- Design and consume features from batch and low‑latency feature stores
- Collaborate on data quality validation
- Support replay and recovery using idempotent design patterns
- Implement feature drift detection
- Model performance monitoring
- SLA and freshness validation
Requirements
- 3–5 years hands‑on experience as a Machine Learning Engineer
- Strong experience taking ML models from development to production
- Programming: Python, PySpark
- ML/MLOps:
- MLflow
- Model versioning and promotion
- Drift detection and monitoring
- Data:
- Feature engineering
- Batch and streaming concepts
- Large‑scale datasets
- AWS experience (preferred):
- S3, Spark/EMR, IAM, basic networking
- Familiarity with:
- Feature stores
- API‑based inference patterns
- Experience with fraud, trust scoring, or risk modeling
- Exposure to PII‑sensitive systems
- Experience migrating batch ML pipelines to real‑time scoring
- Knowledge of explainable ML techniques
Tech Stack
- AWS
- PySpark
- Python
- Spark