[Hiring] AI Engineer @Marlabs

Role Description

As an AI Engineer, you will design, develop, and demonstrate AI and machine learning solutions that address real-world business challenges across:

• Forecasting
• Risk scoring
• Churn prediction
• Anomaly detection
• Optimization

You will work closely with sales engineering, data engineering, and business teams to rapidly translate client requirements into compelling PoCs and scalable AI/ML solutions. The ideal candidate combines strong machine learning expertise with:

• Python development
• Cloud-based AI/ML platforms
• MLOps fundamentals
• Ability to clearly present technical solutions to both technical and non-technical stakeholders

Qualifications

• 1+ years of hands-on AI/ML experience, developing and deploying machine learning models in production, prototype, or PoC environments, particularly using structured or tabular datasets
• Strong proficiency in Python and machine learning libraries, including Pandas, Scikit-learn, XGBoost, CatBoost, LightGBM, NumPy, or similar technologies
• Strong understanding of applied machine learning techniques, including feature engineering, model tuning, cross-validation, error analysis, clustering, anomaly detection, dimensionality reduction, and time-series modeling
• Hands-on experience with cloud-based AI/ML platforms such as AWS SageMaker, Azure Machine Learning, Databricks ML, or comparable technologies
• Working knowledge of MLOps practices, including model versioning, deployment automation, model explainability, performance monitoring, and model/data drift detection
• Strong communication and presentation skills, with the ability to present AI/ML PoCs, technical approaches, model results, and business value to both technical and non-technical stakeholders

Requirements

• Design and develop AI/ML PoCs, prototypes, and demonstrations using structured and tabular data for various business use cases
• Build, train, tune, and evaluate machine learning models using supervised, unsupervised, and semi-supervised techniques
• Develop AI/ML workflows using cloud platforms supporting scalable model experimentation, training, and deployment
• Collaborate with data engineering teams to design data ingestion, preprocessing, feature engineering, and transformation pipelines
• Partner with sales engineering and business stakeholders during pre-sales discussions, demonstrations, and technical workshops
• Apply MLOps and AI engineering best practices across model evaluation, explainability, versioning, deployment, performance monitoring, drift detection, and optimization

Benefits