Job Description
**Senior Machine Learning Engineer** Deliver innovative AI solutions that drive meaningful impact for our clients and society. **Job Description** As a Senior Machine Learning Engineer, you will play a key role in developing advanced machine learning and AI solutions that transform ideas into practical, scalable solutions. You will work closely with data scientists, product developers, and business strategists to design, develop, and deploy machine learning models that create meaningful impact for our clients and society. **Key Responsibilities** • Manage end-to-end machine learning projects, from conceptualization to deployment, ensuring timely delivery and alignment with business objectives. • Design, develop, and deploy machine learning 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. **Experience and Background** • 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++, and other relevant technologies for developing and implementing machine learning algorithms. • Expertise in using PyTorch, 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. **Desirable Skills** • Relevant certifications in data science, machine learning, or big data technologies. • Experience with Microsoft Azure and AWS 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.