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 teams knowledge sharing.
Qualifications:
Experience
: 7+ years of experience in machine learning engineering or a related field.
Technical Skills:
-
Programming Languages
: Proficient in Python and experience with other languages (e.g., Java, Scala, R) is a plus. -
Machine Learning Libraries
: Strong experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, PyTorch, Keras, etc. -
Data Processing
: Experience with data manipulation and processing using libraries like Pandas, NumPy, and Spark. -
Model Deployment
: Experience with model deployment frameworks and platforms (e.g., TensorFlow Serving, TorchServe, Seldon, AWS SageMaker, Google AI Platform, Azure Machine Learning). -
Databases
: Experience with relational and NoSQL databases (e.g., SQL, MongoDB, Cassandra). -
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.