Senior Machine Learning Engineer

About the Role

As a Senior Machine Learning Engineer at Technexus, you will play a crucial role in developing and deploying AI solutions that transform lending and credit decision processes. You'll work at the intersection of finance and artificial intelligence, building models that make fair, accurate, and efficient lending decisions while ensuring explainability and regulatory compliance.

You'll collaborate with cross-functional teams including data scientists, backend engineers, and product managers to integrate machine learning models into our platform. This is an opportunity to make a significant impact on financial inclusion by creating intelligent systems that expand access to credit while maintaining responsible lending practices.

Key Responsibilities

• Design, develop, and deploy machine learning models for credit risk assessment, fraud detection, and lending decisions
• Build and maintain MLOps pipelines for model training, evaluation, and deployment
• Research and implement state-of-the-art machine learning techniques for financial applications
• Develop explainable AI solutions that provide transparency into model decisions
• Collaborate with data engineers to design data pipelines that support ML model requirements
• Analyze model performance and continuously improve models based on real-world feedback
• Work with product and compliance teams to ensure models adhere to regulatory requirements
• Mentor junior data scientists and machine learning engineers

Requirements

• Bachelor's degree in Computer Science, Data Science, Statistics, or related field (Master's or PhD preferred)
• 5+ years of experience building and deploying machine learning models in production environments
• Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes)
• Familiarity with MLOps tools and practices (MLflow, Kubeflow, or similar)
• Experience with financial data and models is highly desirable (credit scoring, risk assessment, etc.)
• Strong understanding of statistical analysis, feature engineering, and model evaluation
• Knowledge of fairness, explainability, and responsible AI practices

Nice to Have

• Experience with NLP and unstructured data analysis
• Familiarity with financial regulations related to algorithmic decision-making (FCRA, ECOA, etc.)
• Experience with graph neural networks and other advanced ML techniques
• Contributions to open-source projects or research publications