Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
- Builds end-to-end data-intensive micro-services, including data pipelines, distributed processing and backend application development
- Designs and implements end-to-end ML engineering solutions, moving experimental models to production scale systems
- Contributes to software engineering communities of practice and events that explore new and emerging technologies
- Adds to team culture of diversity, equity, inclusion, and respect
- Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and proficient applied experience
- Hands-on practical experience in system design, application development, testing, and operational stability
- Proficient in coding in one or more languages
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Overall knowledge of the Software Development Life Cycle
- Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications
- Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies
- Experience in Python or similar programming language, API and backend development
- Practical experience developing Production-scale Cloud-native AI/ML engineering systems in commercial environments
- Familiarity with Kubernetes, MLOps, Docker and Cloud ML/Data engineering services
- Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds
Preferred qualifications, capabilities, and skills
- Familiarity with AWS cloud services for Data and ML
- Experience with developing recommendation systems, NLP services, or other AI/ML systems in large corporate settings
- Prior experience collaborating with data scientists