A0564 Senior Machine Learning Engineer @Dragoneer

We are seeking a ML Engineer to design, build, and deploy ML-powered solutions within the real estate and investment management domain. In this role, you will develop predictive models, scalable pipelines, and automation tools that help business users and analysts extract insights and make smarter, data-driven decisions. You’ll work closely with product managers, data scientists, and data engineers to turn requirements into robust machine learning applications that drive real business outcomes.

About the role

As a ML Engineer, you’ll be responsible for building and operationalizing ML models, integrating them with existing data systems, and ensuring they perform reliably in production. You will collaborate with stakeholders to understand use cases, translate them into machine learning workflows, and design scalable pipelines that handle large volumes of data efficiently. By combining strong engineering skills with an understanding of applied ML, you’ll ensure that predictive analytics, forecasting, and optimization models directly support the client’s real estate, accounting, and investment reporting needs.

• Master’s or PhD in Data Science, Computer Science, or a technology-focused field.
• 4+  years of hands-on experience designing and deploying AI or ML models.
• Hands-on experience setting up and maintaining large-scale data science and machine learning projects.
• Skilled with deep learning libraries like PyTorch, Keras, TensorFlow, and HuggingFace toolkits.
• Solid knowledge of machine learning, especially Natural Language Processing (NLP)  large language models (LLMs).
• Advanced Python coding and strong experience (with tools like Scikit-Learn, NumPy, SciPy, Pandas, and XGBoost.)
• Comfortable using SQL for working with large datasets and uncovering insights.
• Experience creating solutions in cloud platforms such as AWS Sagemaker or Microsoft Azure.
• Able to work on your own or as part of a group to hit targets.

• Curiosity, attention to detail, and drive to solve difficult data problems.
• Focused on getting results for clients and working in an agile development setup.
• Can design or review how a model’s success is measured to line up with business goals.
• Shows initiative by researching new solutions with some guidance.

• Analyze data to find patterns and create machine learning solutions for challenging business issues 
• Understand the AI/ML program journey to formulate relevant high impact business questions that can be answered through data analysis.
• Develop AI/ML solutions that can be scaled across various business use cases, starting from PoC to MVP and launching into production.
• Build and improve AI models - like those for prediction, automation, or natural language tasks.
• Use both in-house tools and the latest technology to increase operational productivity and efficiency as well as predictive analytics.
• Break down and share your process and results in a way that’s clear to non-technical folks, like business managers and executives.
• Keep up with and put into practice the latest AI and machine learning techniques

Work:

• Flexible working hours;
• Collaborative, friendly team environment;
• Remote/Hybrid work;

Life:

• Company social events;
• Annual corporate parties;

Health:

• Comprehensive medical insurance;

Education:

• Allowances for professional education;
• English language courses with native speakers;
• Internal knowledge-sharing sessions.