Summary: APIs are the digital building blocks in enterprises for digitization and integration. SAP API Management, part of SAP Integration Suite, enables its customers to secure, analyze, and scale APIs in a Multi-Cloud environment - AWS, GCP, Microsoft Azure, AliCloud, SAP Data Center & in a Hybrid Deployment [Cloud & On-Premise]. It provides the required visibility & control to consume and monetize APIs with real-time analytics. SAP is evolving its API Platform with significant investments in API-led Integration, Intelligent API Ops - Machine Learning based anomaly detection and remediation, Kubernetes based Provisioning, and in best-in-class experience to explore, discover and consume the APIs via API Hub Enterprise.
The Role: In order to drive this evolution and innovation, we are looking for enthusiastic Machine Learning engineers and innovators, who are passionate about customers, data, technology and product experience, and can join us in our journey to provide the best-in-class API-led Integration Suite and help every customer to become a best-run business.
Technologies We Work On
Programming Languages: Python, Java, Java Script, Node JS, HTML5, Angular, React, Golang
Machine Learning: Tensorflow/Pytorch, Deep Learning techniques like LSTM and Autoencoder, ML Libraries for Time Series Data, NLP and MLOps tools
Analytics: SAP HANA, Elasticsearch/ELK, Grafana
Database: Postgres, Sybase, Cassandra, and SAP HANA
DevOps: Scripting - Shell or Python, Terraform, Ansible, Dynatrace
PaaS & Cloud Infrastructure: Virtualization, Kubernetes, Cloud Foundry, AWS, Azure, Alicloud and GCP
University Degree (B.Tech or M.Tech) in Computer Science or related engineering subject.
Basic understanding of statistical concept; solid data science knowledge; ability to collect, explore and analyze large amount of data.
Research and implement appropriate ML algorithms and tools, create machine learning models from scratch and retraining systems.
Collaborate closely with other applied machine learning engineers and software developer in operations tasks such as ML data management to training, deployment, and monitoring of ML models in production at scale.
Keeping abreast of recent developments in machine learning and familiarity with architectural choices for ML systems.
Working knowledge of MLOps practices and tools is added advantage.
Demonstrate curiosity and innovation in enabling future solutions in this space.
3-10 years of relevant experience in Machine Learning Engineering and Software Development