ML Ops Engineer

Capgemini is hiring for the role of ML Ops Engineer!

Responsibilities of the Candidate:

• Develops the strategies, blueprints and processes for MLOps to be used, while identifying any risks inherent in the life cycle
• Design and build effective, user-friendly infrastructure, tooling, and automation to accelerate Machine Learning at Hyperscale
• Collaborate with teams to drive the ML technical roadmap
• Collaborate with Machine Learning Engineers and Product Managers to develop tools to support experimentation, training and production operations
• Manage & Oversee MLOps life cycle and processes and unify the work of data scientists, data engineers, and software developers
• Collaborate with pre-sales & other teammates to draft responses for client requirements, RFPs etc.
• Engage with the business, leadership, end users, and data experts to scope the project, estimate value, estimate costs
• Develop technical contents and Point of Views, Groom and maintain stakeholder’s relationships
• Establish standards and practices around MLOps, including governance, compliance, and data security

Requirements:

• Understanding of data structures, data modeling, software architecture and computer architecture
• Hands-on experience with ML frameworks, libraries, agile environments, and deploying machine learning solutions using DevOps principles
• Strong programming and coding languages such as Python, SQL, Java, C++, R, JavaScript , etc.
• Experience with machine learning frameworks, libraries and packages like Tensorflow, Keras, PyTorch, scikit-learn, NumPy, Pandas, etc.
• Good understand on the tools serving different purposes in the MLOps pipelines, including Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and analytics, Testing and Cloud Quality tools, as well as network protocols
• Should have good hands-on and know-how of DevOps and MLOps life cycles
• Must know how to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations
• Exposure in at least one cloud (AWS/GCP/Azure) platforms