Looking for a driven and innovative Machine Learning Engineer to help us scale and foresee problems that aren't apparent.

We're seeking a hands-on individual with a strong passion for data and a proven ability to translate complex data challenges into robust, scalable machine learning solutions. In this role, you'll be a key player in developing, deploying, and maintaining ML models that directly impact our core business functions and enhance user experiences.

If you thrive in a collaborative, fast-paced environment, excel at working with diverse data sources, and possess a solid foundation in machine learning principles and MLOps, we encourage you to apply.

Key Responsibilities :

- Design, develop, and implement end-to-end machine learning models, from initial data exploration and feature engineering to model deployment and monitoring in production environments.

- Build and optimize data pipelines for both structured and unstructured datasets, focusing on advanced data blending, transformation, and cleansing techniques to ensure data quality and readiness for modeling.

- Create, manage, and query complex databases, leveraging various data storage solutions to efficiently extract, transform, and load data for machine learning workflows.

- Collaborate closely with data scientists, software engineers, and product managers to translate business requirements into effective, scalable, and maintainable ML solutions.

- Implement and maintain robust MLOps practices, including version control, model monitoring, logging, and performance evaluation to ensure model reliability and drive continuous improvement.

- Research and experiment with new machine learning techniques, tools, and technologies to enhance our predictive capabilities and operational efficiency.

Required Skills & Experience :

- 5+ years of hands-on experience in building, training, and deploying machine learning models in a professional, production-oriented setting.

- Demonstrable experience with database creation and advanced querying (e.g., SQL, NoSQL), with a strong understanding of data warehousing concepts.

- Proven expertise in data blending, transformation, and feature engineering, adept at integrating and harmonizing both structured (e.g., relational databases, CSVs) and unstructured (e.g., text, logs, images) data.

- Strong practical experience with cloud platforms for machine learning development and deployment; significant experience with Google Cloud Platform (GCP) services (e.g., Vertex AI, BigQuery, Dataflow) is highly desirable.

- Proficiency in programming languages commonly used in data science (e.g., Python is preferred, R).

- Solid understanding of various machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction) and experience with advanced techniques like Deep Learning, Natural Language Processing (NLP), or Computer Vision.

- Experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).

- Familiarity with MLOps tools and practices, including model versioning, monitoring, A/B testing, and continuous integration/continuous deployment (CI/CD) pipelines.

- Experience with containerization technologies like Docker and orchestration tools like Kubernetes for deploying ML models as REST APIs.

- Proficiency with version control systems (e.g., Git, GitHub/GitLab) for collaborative development.