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Hace 2 sem
MLOps Engineer – EA, Machine Learning Operations & Cloud
$70,000 - $80,000 Mensual
Sobre el empleo
Descripción
Infotree Global Solutions is a dynamic and entrepreneurial company, comprised of a team of dedicated professionals with over 50 years of direct experience.
We pride ourselves on our high level of customer service and repeat customers. Attention to customer service and innovative solutions in Human Resources, Engineering and IT has helped us to achieve an average yearly growth of over 100% since our founding.
When you talk to Infotree's customers, you will repeatedly hear that it is Infotree's personalized attention and focus to customer needs that has resulted in such strong repeat business.
We are looking for: MLOps Engineer – EA, Machine Learning Operations & Cloud Platforms
• MLOps
• Azure
• Kubernetes
• Docker
• Source Control
• CI/CD
• Machine Learning
Requirements:
o Proficient in Python, and knowledge of C#, and Java, with background knowledge of scripting languages and tools.
o Strong knowledge of machine learning concepts, frameworks and technologies, such as TensorFlow, PyTorch, Scikit-learn, SciPy, NumPy, Pandas, Hugging Face, and OpenAI
o Experience with MLOps tools and practices, such as CI/CD, Docker, Kubernetes, and MLflow
o Experience with SQL and NoSQL database e.g. MSSQL and PostgreSQL
o Proficiency in designing and deploying machine learning models in cloud environments (e.g., Azure/Azure ML, AWS, GCP, AKS, and K8s)
o Experience with building Docker images and managing different environments with centralized container registries.
o Proficient with using K8s for deploying models, inference workloads, and applications.
o Demonstrated expertise in architecting scalable and secure machine learning infrastructure, including data pipelines, storage systems, and model deployment frameworks
o Excellent communication and collaboration skills, with the ability to effectively engage with stakeholders at various levels of the organization
o Ability to multitask and manage multiple activities simultaneously
o Ability to use a wide degree of creativity and latitude to think differently, challenge conventional wisdom, and drive new best practices
o Ability to work effectively with international teams"
Skills:
Responsibilities will include, but not be limited to:
• Collaborate with stakeholders: Engage with business stakeholders, data scientists, software engineers, and other teams to understand their requirements and align machine learning initiatives with overall business goals.
• Integration and interoperability: Work on solutions that seamlessly integrate with existing enterprise systems, databases, and APIs. Collaborate with internal and external partners to enable smooth data flow and interoperability across systems, ensuring consistent and accurate inputs for machine learning models.
• Model deployment and monitoring: Define and implement robust processes for deploying machine learning models into production environments. Establish monitoring mechanisms to track model performance, identify anomalies, and trigger retraining or updates when necessary. Ensure models comply with regulatory and compliance standards.
• Risk assessment and mitigation: Identify potential risks and challenges related to machine learning operations, such as data privacy, security vulnerabilities, or ethical considerations. Propose and implement mitigation strategies to ensure compliance, data integrity, and model robustness.
• Continuous improvement: Continuously evaluate and optimize the machine learning operations infrastructure and processes to improve efficiency, reliability, and scalability. Stay informed about advancements in machine learning operations and recommend new tools, frameworks, or methodologies that can enhance the organization's capabilities.
• Data operations: Build and support data flows for moving or sourcing data sets for training. This includes understanding the data requirements, identifying the best methods for data transfer, and implementing the data pipelines.
• Deploy machine learning models into production environments using Azure Machine Learning Studio, Azure Kubernetes, and Kubernetes on prem.
• Design inference pipeline and set up infrastructure including data storage and data movement, compute instances, and networking.
• Create and manage Azure DevOps (ADO) pipelines to automate the end-to-end machine learning lifecycle.
Implement monitoring and logging solutions to track model performance and detect anomalies with the model, inference pipelines, data movement pipelines, and infrastructure.
Interested? We´d love for you tu get in touch!!
ID: 19599024