SUMMARY
The Software Engineer II is responsible for building, testing, and deploying ML based software applications to solve moderately complex internal and customer facing automation problems. As part of the AI & Analytics Innovation Team, this role will work together with engineers and data scientists to rapidly prototype, evaluate, and deploy solution to solve the most demanding automation challenges. The Software Engineer II will develop applications that drive operational outcomes for internal teams as well as customers. Additionally, this role will identify software issues and recognize / implement additional features for existing software and leverage experience with application development / deployment, data preparation, and API based system integration.
RESPONSIBILITIES, other duties may be assigned.
- Create high-uptime, high-volume data integration systems (MM datapoints / second) to support AI / Analytics systems
- Build, test, deploy API based systems to connect data silos
- Work collaboratively as part of distributed, fast paced team
- Collaborate with the AI & Analytics team on overall system development / testing / deployment
- Develop and optimize API based integrations to support system interoperability
- Rapidly build python-based prototype systems to vet use case feasibility
- Maintain / update deployed API based systems and infrastructure
- Create unit tests for prototype systems to evaluate prototype feasibility / efficacy
- Build / maintain data aggregation services and systems
- Assist with investigating and developing new applications related to critical operations
- Assist with development and population of new database structures
BASIC QUALIFICATIONS
2-6 years of hands-on experience in designing / prototyping / deploying production quality software systems OR MS degree in computer science or equivalent.Demonstrated record of work ethic, team collaboration, accountability, and holding yourself and your team to higher standardsExperience with Python application developmentUnderstanding of data analysis and Extract Transform Load (ETL) best practicesStrong verbal / written communication skillsPREFERRED QUALIFICATIONS
Familiarity with Kubernetes and container orchestration for microservices based architecturesFamiliarity with critical facilities and environmentsExperience with graph, relational, NoSQL, and time series databasesExperience with cloud-based application development (AWS / Azure) and application architectureExperience with traditional Machine Learning model development best practices and toolsKNOWLEDGE, SKILLS AND ABILITIES
Ability to maintain code in production and deploy through modern DevOps pipelinesAbility to identify and implement new features for existing software systems to enhance business outcomesAbility to identify, describe and come up with solutions to identified system deficiencies