- The organization is seeking a best-in-class Principal Software Engineer to lead the technical design, integration, coding and technical & procedural documentation efforts for our Machine Learning Platform and ML Democratization efforts.

- The Principal Software Engineer - ML Platform will drive the platform architecture that will deliver real-time, model driven insights that are accessible on demand as well as meet Machine Learning Operations needs of our Data Science community.

- In partnership with Data Teams and other Technology leadership, you will develop the technical roadmap that leads a team of software engineers, data engineers & data scientists to accomplish the organizations business objective - to design a scalable and reliable platform that serves the needs of data scientists and to democratize the ability to create ML-based solutions to the citizen data scientist, at scale.

- The successful candidate will have a proven track record of delivering large-scale enterprise level platform solutions and will be an active contributor to open-source projects.

- You will have deep knowledge of machine learning processes and will have built multiple complex and scalable, high throughput and low latency machine learning pipelines for both data and algorithm execution.

- You'll have solid experience with building distributed microservices architectures in Java/Golang, at large scale.

- You'll have experience in building solutions for data ingestion and model deployment, training, and testing at scale.

- You have a strong understanding of data and analytics solution architecture, including experience with big data, relational databases, real time and batch data processing

- You have an understanding of security, risk and compliance frameworks, disaster recovery, high availability architectures, hardware, operating systems and networking connectivity

COmpetency:

- Product Evaluation - Knowledge of and ability to implement processes for the evaluation and selection of products, tools, services and infrastructure components ensuring they are in line with the organization's business needs and architectural principles.

- Application Design, Architecture - Knowledge of application design activities, tools and techniques; ability to utilize these to convert business requirements and logical models into a technical application design.

- Packaged (open-source) Application Integration / Prototyping - Knowledge of and the ability to implement packaged application software and integrate it with company applications, databases and technology platforms.

- Software Process Improvement (SPI) - Knowledge of formal software process improvement disciplines, and ability to assess and improve the quality and operating costs associated with an existing application.

- System Development Life Cycle - Knowledge of product management techniques and the ability to plan, design, develop, test, implement and maintain system development life cycle segments and phases.

- Technical Troubleshooting - Knowledge of technical troubleshooting approaches, tools and techniques, and the ability to anticipate, recognize, and resolve technical (hardware, software, application or operational) problems

Technologies/Frameworks/Solutions:
- TensorFlow, Scikit-learn, Karas, PyTorch, SparkML, Horovod, mlflow, MLeap, Kubeflow, AutoGluon, H20.ai and similar solutions
- Git, Artifactory, Maven, Jenkins, Docker, Kubernetes, Spinnaker and similar technologies
- Python, GoLang, Java, etc.
- Hadoop, InfluxDB, Postgres, MongoDB etc