Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
About the role
- Senior ML Engineer responsible for designing and building ML pipelines for a Trust Scoring platform. Involves productionizing models and implementing MLOps best practices.
Responsibilities
- 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, schema contracts, drift detection (feature + score)
- Implement feature drift detection, model performance monitoring, SLA and freshness validation
- Support replay and recovery using idempotent design patterns
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
- Nice to Have
- 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
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