Machine Learning Engineer

 

We are seeking a skilled Machine Learning Engineer to design and implement ML solutions for mortgage industry challenges. You'll work on complex projects with growing independence and provide technical recommendations to business stakeholders. This role offers significant growth potential for someone ready to advance their technical ownership while building expertise in mortgage lending applications of machine learning. 

WHAT YOU WILL BE DOING

• Design and implement ML solutions for mortgage challenges by following established architectural guidelines while contributing to technical decision-making and solution design. 

• Lead or significantly contribute to complex projects from conception to deployment, increasing ownership of technical decisions. Collaborate with senior engineers on project scope, success metrics, and technical approach. 

• Analyze mortgage business processes and identify opportunities for ML improvement. Work with business stakeholders to translate requirements into technical solutions and provide data-driven recommendations. 

• Develop ML approaches from basic ML models, ensemble methods to deep learning architectures and natural language processing for document analysis - “Use the right tool for the right job”.  

• Implement model interpretability techniques to support regulatory compliance and business communication. 

• Build robust, scalable MLOps pipelines with proper testing, monitoring, and governance. Implement good coding practices including error handling, logging, and appropriate testing strategies for production mortgage applications. 

• Work closely with senior team members and other teams/SMEs across the organization while mentoring junior engineers as opportunities arise.  

• Participate in technical discussions, code reviews, and best practice development across the team. 

• Apply understanding of mortgage processes, regulations, and risk factors to inform ML model design and feature engineering. Learn and adapt to regulatory requirements and compliance standards. 

WHAT WE NEED FROM YOU

Required Qualifications:

• Education: Master’s degree in Computer Science, Data Science, Engineering, or related field (or Bachelor’s degree with relevant work experience in Machine Learning/ Data Science) 

• Experience: 5+ years of machine learning experience with some exposure to production ML systems and project ownership 

• ML Fundamentals: Strong foundation in ML algorithms, statistical modeling, and at least one deep learning framework. Basic experience with natural language processing techniques for text analysis. Experience selecting appropriate techniques for business problems and handling common data challenges. 

• Programming: Proficient in Python programming with experience in production-oriented code development. Familiarity with software engineering best practices, DevOps principles, and code quality principles. 

• Model Development: Experience in using any one of the major Cloud ML frameworks (Azure ML Studio, Vertex AI, Amazon Sagemaker) for model training and deployment 

• Model Deployment: Experience with model deployment to production environments, including basic knowledge of model serving, API development, containerization, and deployment pipelines. 

• Problem Solving: Demonstrated ability to work on complex technical problems with guidance and increasing independence. Experience breaking down business problems into technical approaches. 

• Communication: Good communication skills with the ability to explain technical concepts to different audiences and collaborate effectively in cross-functional teams. 

• Learning Mindset: Eagerness to learn about mortgage industry processes, regulations, and business metrics. Adaptability to new technologies and methodologies. 

Preferred Qualifications: 

• Financial Services: Previous experience in mortgage lending, banking, or financial services 

• Advanced ML: Experience with different Model deployment patterns, advanced NLP techniques, as well as transfer learning and fine-tuning. 

• MLOps: Experience with model deployment, versioning, monitoring practices, and production model lifecycle management 

• Cloud & Scale: Experience with cloud-native scalable development of ML models. 

• Business Impact: Examples of translating business problems into successful ML solutions 

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