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Job Description
Senior Machine Learning Engineer
Develop advanced machine learning and AI solutions, contributing to our AI strategy, and driving innovation.
About the Role
- Manage end-to-end machine learning projects, from conceptualisation to deployment, ensuring timely delivery and alignment with business objectives.
- Design, develop, and deploy machine learning (ML) models to create scalable AI solutions.
- Perform comprehensive data preparation, including cleaning, preprocessing, and feature engineering to ensure high-quality input data.
- Work with data scientists, software engineers, and product managers to integrate machine learning models into production systems.
- Provide guidance and mentorship to junior machine learning engineers and data scientists, fostering skill development and knowledge sharing.
Requirements
- Minimum of 4+ years of hands-on experience in building and deploying real-world machine learning (ML) models and solutions.
- Proficiency in Python, R, C++, other relevant technologies for developing and implementing machine learning algorithms.
- Expertise in using Py Torch, scikit-learn, Apache Spark, or other equivalent technologies for building and training models.
- Knowledge of Hadoop, Kafka, or other big data technologies for processing and managing large datasets.
- Knowledge of SQL and No SQL databases for data storage and retrieval.
- Knowledge of MLOps practices and tools for managing the entire machine learning lifecycle, from data collection to model deployment and monitoring.
Preferred Qualifications
- Relevant certifications in data science, machine learning or big data technologies.
- Experience with Microsoft Azure (and AWS, if available) for deploying and scaling machine learning apps.
- Experience building machine learning solutions using Databricks or other equivalent platforms.
- Understanding of best practices for ensuring the security and compliance of machine learning applications, including data privacy and ethical considerations.
- Familiarity with advanced machine learning techniques and frameworks, such as reinforcement learning, natural language processing (NLP), computer vision.
- Experience with Docker, Kubernetes, or other equivalent technologies for deploying ML models in production.