PivotX Advisors
We are seeking a Senior Machine Learning Engineer with deep expertise in MLOps to lead the design, development, and deployment of scalable machine learning models and infrastructure. In this role, you will collaborate with data scientists, data engineers, software engineers, and DevOps teams to build, maintain, and optimize the entire lifecycle of machine learning systems from pilot to production.
The core responsibilities for the job include the following:
Model Development and Deployment
Design, develop, and deploy robust machine learning models for various use cases such as classification, regression, and deep learning models. Lead the full ML lifecycle, from model training and experimentation to production deployment and post-deployment monitoring. MLOps Integration
Design and implement scalable MLOps pipelines for automating the end-to-end machine learning lifecycle (data preparation, model training, testing, deployment, monitoring, and versioning). Collaborate with DevOps to build and manage CI/CD pipelines for machine learning models and APIs. Optimize model performance and ensure scalability of model-serving architecture (e. g., Kubernetes, Docker, AWS/GCP/Azure). Automate data pipelines and ensure smooth orchestration of workflows using tools like Airflow, Kubeflow, or MLFlow. Collaboration and Mentorship
Work closely with data scientists and engineering teams to integrate ML models into production systems. Lead and mentor junior engineers on best practices in machine learning, software engineering, and MLOps. Act as a bridge between data science and engineering teams to ensure the seamless operation of machine learning systems. Performance Monitoring and Optimization
Implement and maintain tools for monitoring, alerting, and managing ML model performance in production (e. g., Prometheus, Grafana). Continuously improve model reliability, accuracy, and efficiency through rigorous testing, validation, and retraining. Security and Compliance
Ensure models adhere to security best practices and comply with industry standards for data privacy and regulatory requirements. Establish proper version control for models and data to support compliance and auditing needs. Requirements
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. 5+ years of hands-on experience in designing, developing, and deploying machine learning systems. 3+ years of experience in MLOps practices, including building and managing pipelines for continuous integration and continuous delivery (CI/CD). Proven track record of delivering high-quality machine learning products into production at scale. Technical Skills
Deep understanding of ML algorithms, statistical modeling, and deep learning frameworks (e.g. TensorFlow, PyTorch, Scikit-Learn). Experience with MLOps frameworks and tools like MLflow, Kubeflow, Airflow, Seldon, and Tecton. Experience in DevOps tools and practices, including CI/CD, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform). Strong experience with cloud services (AWS, GCP, or Azure), specifically around ML services like SageMaker, Vertex AI, or Azure ML. Proficiency in Python and familiarity with other languages such as Java, Scala, or R. Experience with data pipelines, ETL/ELT processes, and databases (SQL and NoSQL). Soft Skills
Excellent problem-solving skills with a focus on delivering high-impact solutions. Strong communication skills, with the ability to work effectively in cross-functional teams. Demonstrated leadership and mentorship abilities.
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Design, develop, and deploy robust machine learning models for various use cases such as classification, regression, and deep learning models. Lead the full ML lifecycle, from model training and experimentation to production deployment and post-deployment monitoring. MLOps Integration
Design and implement scalable MLOps pipelines for automating the end-to-end machine learning lifecycle (data preparation, model training, testing, deployment, monitoring, and versioning). Collaborate with DevOps to build and manage CI/CD pipelines for machine learning models and APIs. Optimize model performance and ensure scalability of model-serving architecture (e. g., Kubernetes, Docker, AWS/GCP/Azure). Automate data pipelines and ensure smooth orchestration of workflows using tools like Airflow, Kubeflow, or MLFlow. Collaboration and Mentorship
Work closely with data scientists and engineering teams to integrate ML models into production systems. Lead and mentor junior engineers on best practices in machine learning, software engineering, and MLOps. Act as a bridge between data science and engineering teams to ensure the seamless operation of machine learning systems. Performance Monitoring and Optimization
Implement and maintain tools for monitoring, alerting, and managing ML model performance in production (e. g., Prometheus, Grafana). Continuously improve model reliability, accuracy, and efficiency through rigorous testing, validation, and retraining. Security and Compliance
Ensure models adhere to security best practices and comply with industry standards for data privacy and regulatory requirements. Establish proper version control for models and data to support compliance and auditing needs. Requirements
Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field. 5+ years of hands-on experience in designing, developing, and deploying machine learning systems. 3+ years of experience in MLOps practices, including building and managing pipelines for continuous integration and continuous delivery (CI/CD). Proven track record of delivering high-quality machine learning products into production at scale. Technical Skills
Deep understanding of ML algorithms, statistical modeling, and deep learning frameworks (e.g. TensorFlow, PyTorch, Scikit-Learn). Experience with MLOps frameworks and tools like MLflow, Kubeflow, Airflow, Seldon, and Tecton. Experience in DevOps tools and practices, including CI/CD, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code (Terraform). Strong experience with cloud services (AWS, GCP, or Azure), specifically around ML services like SageMaker, Vertex AI, or Azure ML. Proficiency in Python and familiarity with other languages such as Java, Scala, or R. Experience with data pipelines, ETL/ELT processes, and databases (SQL and NoSQL). Soft Skills
Excellent problem-solving skills with a focus on delivering high-impact solutions. Strong communication skills, with the ability to work effectively in cross-functional teams. Demonstrated leadership and mentorship abilities.
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