About the Role :

We are seeking a highly skilled and experienced Machine Learning Engineer to join our dynamic team.

As a Machine Learning Engineer, you will be responsible for the design, development, deployment, and maintenance of machine learning models and systems that drive our [mention specific business area or product, e.g., recommendation engine, fraud detection system, autonomous vehicles].

You will work closely with data scientists, software engineers, and product managers to translate business needs into scalable and reliable machine learning solutions.

This is a key role in shaping the future of CBRE and requires a strong technical foundation combined with a passion for innovation and problem-solving.

Responsibilities :

Model Development & Deployment :

- Design, develop, and deploy machine learning models using various algorithms (e.g., regression, classification, clustering, deep learning) to solve complex business problems.

- Select appropriate datasets and features for model training, ensuring data quality and integrity.

- Implement and optimize model training pipelines, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.

- Deploy models to production environments using containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP, Azure).

- Monitor model performance in production, identify and troubleshoot issues, and implement model retraining and updates as needed.

Infrastructure & Engineering :

- Develop and maintain APIs for model serving and integration with other systems.

- Write clean, well-documented, and testable code.

- Collaborate with software engineers to integrate models into existing products and services.

Research & Innovation :

- Stay up to date with the latest advancements in machine learning and related technologies.

- Research and evaluate new algorithms, tools, and techniques to improve model performance and efficiency.

- Contribute to the development of new machine learning solutions and features.

- Proactively identify opportunities to leverage machine learning to solve business challenges.

Collaboration & Communication :

- Collaborate effectively with data scientists, software engineers, product managers, and other stakeholders.

- Communicate technical concepts and findings clearly and concisely to both technical and non-technical audiences.

- Participate in code reviews and contribute to the team's knowledge sharing.

- Version Control : Experience with Git and other version control systems.

- DevOps : Familiarity with DevOps practices and tools.

Strong understanding of machine learning concepts and algorithms : Regression, Classification, Clustering, Deep Learning etc.

Soft Skills :

- Excellent problem-solving and analytical skills.

- Strong communication and collaboration skills.