Responsibilities
- End-to-End Development: Design, develop, test, and deploy features and services within the Vantage platform, contributing to areas such as document processing pipelines, AI skill management, cloud infrastructure, and API layers.
- Code Quality: Write clean, maintainable, well-tested code; actively participate in code reviews and champion engineering best practices across your team.
- Cross-Functional Collaboration: Work closely with product managers, UX designers, ML engineers, and fellow software engineers to translate Vantage feature requirements into well-architected solutions.
- Continuous Improvement: Identify and surface improvements to existing Vantage services, tooling, and delivery workflows; contribute to reducing technical debt and increasing engineering velocity.
- System Reliability: Monitor the health and performance of Vantage cloud services; investigate and resolve production incidents in a timely manner.
- Technical Growth: Stay current with advances in Document AI, cloud-native patterns, and GenAI integration approaches that could enhance the Vantage platform.
Requirements
- 5–9 years of professional software engineering experience with deep expertise in C++
- Demonstrated ability to work with multiple programming languages, and willingness to work with both C# .NET and C++
- Proven experience designing, implementing, and operating CI/CD pipelines for building, testing, and deploying workflows using GitHub Actions and/or Azure DevOps Pipelines
- Good understanding of microservices architecture, distributed system design, and cloud-native patterns at enterprise scale
- Demonstrated track record of delivering high-quality, testable code
- Strong analytical and debugging skills with the ability to investigate complex issues across distributed systems
Nice to Have
- Experience in cloud, preferably using Microsoft Azure (AKS, Storage, Key Vaults, Azure SQL or equivalent)
- Hands-on experience designing and operating workflow-driven systems (orchestration, retries, timeouts, and state management)
- Experience integrating Machine Learning, Neural Networks or LLMs into enterprise applications and workflows
- Familiarity with OCR, NLP and computer vision technologies
- Comfort operating across a globally distributed engineering organization spanning multiple regions and cultures