Machine Learning Engineer – Risk Scoring, Model Monitoring

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🔥 0 minutes ago

💃 Latin America – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 25%

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Chiri

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2025

🤖 Artificial Intelligence

🤝 B2B

☁️ SaaS

Artificial Intelligence • B2B • SaaS

Chiri is an AI managed service provider (MSP) that builds company-specific AI applications and agents on a shared platform called Chiri Brain. They embed small, senior pods of engineers and operators inside client teams to scope work, deliver MVPs, connect to existing software, and scale to enterprise production while retaining a persistent organizational memory (Ontology), integration layer (Sherpa), and security/validation layer (Glacier). Their offering emphasizes avoiding vendor lock-in to specific models by keeping context model-independent, delivering governance, permissions, auditability, and AI infrastructure. Chiri targets businesses seeking to move AI from pilots to production, providing ROI-based scoping, short time-to-MVP, and ongoing operational support with the Brain platform carrying the capability after the pod steps back.

📋 Description

• Develop and evaluate predictive models using XGBoost, LightGBM, and CatBoost for risk scoring, classification, and related decision support • Compare machine learning performance against deterministic rules and historical benchmarks • Detect changes in input distributions, prediction behavior, and model accuracy, with alerts when performance falls below defined thresholds • Build feedback loops using newly available outcome data, with retraining and validation procedures • Identify and prevent target leakage, temporal leakage, proxy leakage, and unintended bias in model features • Implement reproducible model pipelines, monitoring, performance reporting, explainability, and controlled model deployment • Determine where machine learning adds measurable predictive value to a healthcare and pharmacy-finance platform • Explain model performance, limitations, and business implications to technical and nontechnical stakeholders

🎯 Requirements

• Strong production machine learning experience using Python • Hands-on expertise with XGBoost, LightGBM, CatBoost, or comparable gradient-boosted tree frameworks • Deep understanding of statistics, feature engineering, model evaluation, calibration, and temporal validation • Experience detecting and managing data drift, concept drift, model degradation, and retraining triggers • Proven experience preventing data leakage and identifying proxy variables that compromise model validity • Experience developing production ML pipelines with automated monitoring, testing, and reproducibility • Ability to explain model performance, limitations, and business implications to technical and nontechnical stakeholders • Experience with healthcare, pharmacy, financial risk, credit scoring, or claims analytics • Experience with delayed outcome labels and historical transaction datasets • Experience deploying interpretable models in regulated environments • Experience combining deterministic rules with probabilistic models

🏖️ Benefits

• The opportunity to help establish a rigorous, measurable approach to machine learning in a high-stakes business environment.

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