MLOps Engineer
at DXC Technology
Oeiras, Área Metropolitana de Lisboa, Portugal -
Start Date | Expiry Date | Salary | Posted On | Experience | Skills | Telecommute | Sponsor Visa |
---|---|---|---|---|---|---|---|
Immediate | 17 Sep, 2024 | Not Specified | 18 Jun, 2024 | N/A | Automation,Azure,Cloud Computing,Enterprise Systems,Soft Skills,Devops,Big Data Analytics,Communication Skills,Maintainability,Data Science,Data Processing,Archimate,Datasets,Docker,Togaf,Design,Security,Production Systems,Virtual Machines | No | No |
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Description:
JOB DESCRIPTION:
- Provide experienced technical consulting and delivers solutions within teams to optimize performance
- Design and develop architectures, proof-of-concepts and demos in support of presales activities for clients
- Act as a technical lead in Machine Learning projects, ensuring production scale machine learning applications
- Contribute as technical expert in presales activities and knowledge sharing in the DXC machine learning community
EDUCATION AND COMPETENCIES
- Bachelor or Masters degree required in computer science or equivalent qualification
- Hands-on experience in delivering and leading Data Science and machine learning projects
- Proven experience in all phases of a Big Data/Analytics project: Concept & design, development, implementation, change and operation
- Advanced experience in programming (Python, SQL, bash)
- Experience with Docker, Kubernetes/AKS/EKS/Openshift
- Experience with DevOps, CI/CD and MLOps automation
- Experience in cloud architecting and machine learning technologies – Azure/AWS/GCP
- Experience in designing data management solution architectures
- Experience in designing machine learning solution architectures
- Experience in applying ArchiMate and TOGAF
- Experience with data versioning, pipeline tools and model repositories, e.g. Kubeflow, dvc, mlflow or cloud equivalents
REQUIREMENTS
- Design Data Pipelines and Engineering Infrastructure: You’ll create and maintain the necessary data pipelines to support large-scale ML enterprise systems
- Transition Offline Models to Production Systems: Take ML models developed by data scientists and transform them into real production systems
- Develop and Deploy Scalable Tools: Build scalable tools and services so that our clients can manage ML model training and inference
- Evaluate New Technologies: Stay up-to-date with the latest trends and evaluate new technologies to enhance the performance, maintainability, and reliability of client ML systems
- Apply Software Engineering Best Practices: Use practices like continuous integration (CI/CD), automation, and auditing to ensure quality and security in ML systems
- Facilitate Proof-of-Concept Model Development and Deployment: Collaborate with other teams to carry out proof-of-concept projects and demonstrate the value of ML solutions
- Knowledge of Machine Learning and Data Science: You should have a strong understanding of machine learning algorithms and data science concepts is necessary. This includes data cleaning, preprocessing, feature engineering, model training, and evaluation
- Experience with Spark: Experience with Spark for big data processing is important. This includes knowledge of Spark SQL, DataFrames, and MLlib
- Cloud Computing: Deep understanding of cloud computing concepts, including virtual machines, containers, serverless computing, and cloud storage
- Experience with Machine Learning Frameworks: Familiarity with frameworks like TensorFlow, PyTorch, Keras, etc., is important
- Familiarity with MLOps Tools: Experience with tools like Kubeflow, MLflow, TFX, Seldon, etc., that help in the deployment, monitoring, and maintenance of machine learning models
- Knowledge of DevOps Practices: Understanding of DevOps practices like CI/CD is important. Familiarity with GitHub and Azure DevOps would be a plus
- Soft Skills: Good communication skills to explain complex concepts to non-technical stakeholders, problem-solving skills, and ability to work in a team are also important
- Proficiency in Azure Machine Learning: You should be proficient with Azure Machine Learning platform, including creating and managing workspaces, experiments, compute resources, and datasets
- Experience with Azure Databricks: You should have experience with Azure Databricks for big data analytics. This includes knowledge of creating Databricks workspaces, clusters, notebooks, jobs, and tables
- Familiarity with Azure Services: You should be familiar with other Azure services that can be integrated with Azure Machine Learning and Databricks, such as Azure Data Factory, Azure Synapse Analytics, Azure Storage, etc
Responsibilities:
Please refer the Job description for details
REQUIREMENT SUMMARY
Min:N/AMax:5.0 year(s)
Information Technology/IT
IT Software - Other
Software Engineering
Graduate
Computer science or equivalent qualification
Proficient
1
Oeiras, Portugal