Machine Learning Engineer

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.

Qualifications:

Experience

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.