Technical Architect Artificial Intelligence (AI) & Machine Learning (ML)
To lead the Artificial Intelligence (AI) group and to foster Digital R&D collaborations across the product groups. This group helps to solve the organization's most difficult challenges based on leading AI, ML & Big Data technologies. You will build prototypes and explore conceptually new solutions in a fast-paced environment. This is a hands-on, highly technical position.
- BS in Computer Science or Engineering with formal qualification in Maths andor statistics
- 3+ years of experience in the fields of Natural Language Processing, Dialog System, Contextual Understanding, Machine Learning, Deep Learning, Computer Vision
- 3+ years of experience with Python, Scala, Java, Node.js
- 2+ years of experience in Apache Spark, Apache Hadoop, Kafka, Java Spring, Elastic Map Reduce
- 1+ years of experience in one or more AI platform, e.g. IBM Watson, Google Api.Ai, Facebook Wit.Ai, Microsoft Bot Framework
- Experience in one or more of Microsoft Azure, GCP, AWS and Continuous Integration & Continuous Training into these platforms
- Experience in one or more of Spark MLlib, TensorFlow, PyTorch, Apache MXNet, XGBoost
- Advanced degree in computer science, engineering or mathematics
- 2+ years hands-on experience working with AWS or other cloud/containerized platforms
- Minimum 12 year of relevant IT experience.
- Extract qualitative and quantitative relationships, including patterns and trends from a wide variety of data
- Use technical and analytical expertise to explore and examine data from multiple disparate Big Data sources with the goal of discovering patterns and previously hidden insights that can provide a competitive advantage for our products
- Analyze and implement analysis infrastructure and tools, analytic workflow processes, and complex data visualizations and leverage foundational comprehension of mathematical and statistical concepts, algorithm development, feature engineering, model selection and training, testing and validation of different models