Job Responsibilities:
- Architect and develop scalable Python-based systems that support ML-driven risk classification, tagging, and approval triage
- Integrate ML models into microservices and APIs for use within AI Judge workflows
- Lead engineering design reviews, establish coding standards, and ensure system robustness and security
- Build and maintain feature pipelines and model-serving infrastructure using cloud-native tools
- Work closely with ML scientists, data engineers, and product managers to align on requirements and delivery timelines
- Drive engineering quality, CI/CD integration, observability, and unit testing for AI-enabled software components
- Mentor junior engineers and uphold engineering excellence across the team
Required Qualifications, Capabilities, and Skills:
- Master's degree in computer science, Software Engineering, or related field
- 6+ years of experience as a backend or AI/ML software engineer
- Proficiency in Python with deep experience in building distributed and containerized services (e.g., Flask/FastAPI, Docker, Kubernetes)
- Strong understanding of ML deployment workflows, feature engineering, and serving architectures
- Experience building and deploying APIs and ML inference services in production
- Familiarity with ML model management, versioning, and performance monitoring
- Strong engineering fundamentals: data structures, system design, testing, and performance optimization
- Excellent communication and collaboration skills across technical and non-technical teams
Preferred Qualifications, Capabilities, and Skills:
- Experience with AWS cloud stack (S3, SageMaker, Lambda, ECS, etc.)
- Experience working with structured data, tabular models, and metadata-driven platforms
- Experience with regulated data systems, enterprise controls, or secure data processing workflows
- Contributions to open-source ML or backend tooling frameworks