VeeRteq Solutions LLC
Machine Learning Engineer
VeeRteq Solutions LLC, Washington, District Of Columbia, United States, 20022
Experience:
5+ Years
Location:
Remote As a
Machine Learning Engineer , you will work as part of an Agile team to build cutting-edge healthcare applications and implement new features while following industry best practices and coding standards. What's in it for you?
Responsibilities
Partner with business stakeholders to identify opportunities for automating business processes using Machine Learning and AI. Collaborate with project managers, DevOps, and engineering teams to coordinate model development, deployment, and monitoring. Design, develop, and optimize machine learning models and algorithms for various business use cases. Analyze complex datasets to extract actionable insights and build predictive models. Write clean, efficient, and scalable code using Python, Spark, Scala, R, and Java. Deploy ML models into production using CI/CD pipelines and containerization tools. Continuously monitor and maintain model performance, implementing retraining as necessary. Deliver analytics solutions and insights to clients, enabling data-driven decision-making. Build dashboards and visualizations using Tableau to effectively communicate insights. Leverage cloud ML platforms (AWS SageMaker, Azure ML, or Google Cloud AI) for scalable model development. Follow best practices in version control, testing, and documentation using Git and shell scripting. Ensure adherence to robust software architecture and data modeling standards. Required Skills
Proven experience as a Machine Learning Engineer or in a similar role. Strong understanding of data structures, data modeling, and software architecture. Deep knowledge of mathematics, probability, statistics, and machine learning algorithms. Proficiency in programming languages: Python, Spark, Scala, R, Java. Hands-on experience with ML frameworks: TensorFlow, PyTorch, Keras, scikit-learn. Familiarity with Docker, shell scripting, and Git. Experience deploying ML models in production environments. Experience with cloud ML platforms (AWS, Azure, or Google Cloud). Excellent communication and collaboration skills. Strong analytical and problem-solving abilities. Preferred Skills
Experience with MLOps tools and practices. Exposure to real-time data processing and streaming technologies. Experience with data visualization tools like Tableau. Direct experience working with clients to deliver analytics solutions. Educational Qualifications
Master's Degree Technical certifications in multiple technologies are desirable.
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5+ Years
Location:
Remote As a
Machine Learning Engineer , you will work as part of an Agile team to build cutting-edge healthcare applications and implement new features while following industry best practices and coding standards. What's in it for you?
Responsibilities
Partner with business stakeholders to identify opportunities for automating business processes using Machine Learning and AI. Collaborate with project managers, DevOps, and engineering teams to coordinate model development, deployment, and monitoring. Design, develop, and optimize machine learning models and algorithms for various business use cases. Analyze complex datasets to extract actionable insights and build predictive models. Write clean, efficient, and scalable code using Python, Spark, Scala, R, and Java. Deploy ML models into production using CI/CD pipelines and containerization tools. Continuously monitor and maintain model performance, implementing retraining as necessary. Deliver analytics solutions and insights to clients, enabling data-driven decision-making. Build dashboards and visualizations using Tableau to effectively communicate insights. Leverage cloud ML platforms (AWS SageMaker, Azure ML, or Google Cloud AI) for scalable model development. Follow best practices in version control, testing, and documentation using Git and shell scripting. Ensure adherence to robust software architecture and data modeling standards. Required Skills
Proven experience as a Machine Learning Engineer or in a similar role. Strong understanding of data structures, data modeling, and software architecture. Deep knowledge of mathematics, probability, statistics, and machine learning algorithms. Proficiency in programming languages: Python, Spark, Scala, R, Java. Hands-on experience with ML frameworks: TensorFlow, PyTorch, Keras, scikit-learn. Familiarity with Docker, shell scripting, and Git. Experience deploying ML models in production environments. Experience with cloud ML platforms (AWS, Azure, or Google Cloud). Excellent communication and collaboration skills. Strong analytical and problem-solving abilities. Preferred Skills
Experience with MLOps tools and practices. Exposure to real-time data processing and streaming technologies. Experience with data visualization tools like Tableau. Direct experience working with clients to deliver analytics solutions. Educational Qualifications
Master's Degree Technical certifications in multiple technologies are desirable.
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