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hace 2 meses
Empresa reconocida is a leading digital freight forwarder that leverages data and machine learning to revolutionize the logistics industry. Our data-centric approach enables us to build innovative solutions that drive business growth and efficiency.
Job SummaryWe are seeking a highly skilled Senior MLOps Engineer to join our central Machine Learning Engineering team. As a key member of our team, you will be responsible for designing and developing health and performance monitoring tools for our data pipelines and machine learning services in production.
Key Responsibilities- Define the new state-of-the-art for machine learning engineering in the road logistics services;
- Design and develop health and performance monitoring tools (MLOps) of data pipelines and the machine learning services in production;
- Design and improve heterogeneous, asynchronous, and high-performance large-data processing pipelines from/to multiple sources/destinations;
- Operationalize innovative, data-intensive, end-to-end machine-learning (ML)-based decision engines;
- Highly motivated with excellent communication and strong interpersonal skills;
- In-depth knowledge and professional working experience with software engineering, ETL pipelines, and setting up machine learning workflows;
- Solid Python software engineering skills, including best practices like CI/CD and Git;
- Experience in designing/implementing virtualization services (e.g.: Docker/Kubernetes/Lambda Functions), microservices architectures (e.g.: RESTful API with Flask), and Kafka messaging queues in AWS cloud ecosystem;
- T-shaped mindset: actual expertise in a single area but the ownership/willingness to contribute to the product end to end to ensure a healthy value creation chain;
- Team-oriented. We don't believe in superheroes but rather in super-teams: teams that own products and are the single unit of work :)
- Experience with Agile philosophies (e.g.: Scrum, Scrumban, Kanban, XP) and project management tools (e.g.: JIRA);
- Fluent written and verbal communication in English;
- 2+ years of experience in deploying and maintaining in production data pipelines working at scale which are fueling and/or being fueled by machine learning models in production;
- Basic understanding of machine learning product lifecycle and the commonly associated components (MLOps): Experimental Environment (e.g.: Jupyter Notebook, MLflow), Workflow management (e.g.: Airflow), Feature Stores (e.g.: Feast), DataOps/Pipelines (e.g.: Kafka), Model Deployment (e.g.: Terraform), Testing, Serving (e.g.: Docker, Flask), and Monitoring (e.g.: Datadog), Model Repository (e.g.: DVC). The list of technologies/methodologies is for illustrative purposes. You are not expected to have experience in each single one of these technologies/methodologies.
