MLops Engineer

Job 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
   

What We Offer
 

• Full-time employment contract
• Numerous opportunities for personal and professional development and advancement within the company by taking part in interesting and challenging projects
• Modern and friendly work environment with open door policy
• Professional technical and soft skill training programs (internal DXC University, DXC Partners Network and certification program), opportunity to learn and evolve within a team of experienced colleagues
• Private medical care, social benefits system, life insurance
• Competitive compensation package adjusted to candidate’s experience and qualifications
• Flexibility in work arrangement

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