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.(ref : hirist.tech)