Machine Learning Engineer – Mid/Senior

🕒 June 26

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Kognit - Committed to Transforming

WebsiteLinkedInAll Job Openings

51 - 200 employees

🤝 B2B

🤖 Artificial Intelligence

Software • B2B • Artificial Intelligence

Kognit is a company specialized in creating unique, tailored, and innovative digital solutions for the corporate market. With 18 years of experience, Kognit helps global companies leverage technology to positively transform their businesses, markets, and the world. The company coordinates and executes projects globally, delivering innovative and scalable software solutions that provide high added value. Kognit is committed to understanding business objectives and providing insights that assist clients in achieving positive transformations in their operations and markets.

📋 Description

• Develop tabular ML models for operational decision-making: risk classification (XGBoost), dynamic pricing (gradient boosting), and combinatorial assignment optimization (Google OR-Tools). • Operate models in shadow mode with human-in-the-loop (HITL) validation before any automation. • Build the MLOps pipeline: training in Azure ML, model registry in MLflow, inference deployment (FastAPI/Docker) on AWS, with environment promotion and rollback. • Define metrics and backtesting with point-in-time correctness using the Feature Store. • Implement drift monitoring, automated retraining, and canary/A-B deployment via CI/CD. • Serve models via low-latency inference APIs (online features in Redis), with OpenAPI contracts.

🎯 Requirements

• Bachelor's degree in Data Science, Computer Science, Statistics, Engineering, Mathematics, Economics, or related fields. • English or Spanish (international client). • Strong Python skills for production ML (not just notebooks). • Experience with tabular models using gradient boosting (XGBoost or similar) — imbalanced classification, probability calibration, regression. • Model evaluation and backtesting: AUC/Recall/Precision, MAPE/MAE/RMSE, temporal validation and point-in-time correctness. • MLOps: model versioning and registry (MLflow or equivalent), training/deploy pipelines in CI/CD, environment promotion and rollback. • Model serving via API (FastAPI or equivalent) in Docker containers. • SQL for data extraction and preparation.

🏖️ Benefits

• Contractor — PJ (we are looking for long-term partners). • Remote work. • Working hours: 8 hours per day (168 hours monthly). • Time bank with quarterly settlement (payment for positive balance hours). • Variable compensation after 4 months of contract. • Flexible benefits card: BRL 700.00 per month for meals (Flash card). • Paid leave: 7 consecutive days after 1 year of contract; 15 days after 2 years; 21 days between 5 and 7 years; and 30 days from 8 years onward. • Birthday day off: one day off in your birthday month. • Partnership with Wellhub. • Partnership with Open English. • Partnership with FIAP. • Partnerships with clinics and psychological services.