Python Fast API Engineer

Location: Remote Python FastAPI Engineer (ML + Risk Modeling) "Strong hands-on experience with Python and FastAPI (building production REST services). Experience serving or integrating ML models in production (risk scoring, classification/regression, or similar). Solid engineering practices: unit/integration testing, Git-based workflows, CI/CD, and API documentation (OpenAPI/Swagger). Familiarity with model governance concepts: version control/lineage, monitoring, explainability/reason codes, audit logs. Experience with AWS and/or model execution environments such as SageMaker" "Design and develop REST APIs using Python + FastAPI to serve real time model scoring for individual loans/customers and support batch scoring workloads. Integrate APIs with upstream/downstream systems and ensure strong input validation and error handling. Support model hosting & execution for Python-based models and enable flexible deployment patterns Implement model operational capabilities: versioning/lineage, audit trails, reason codes/explainability hooks, and champion challenger support where needed. Build/enable observability: structured logging, metrics, tracing, and monitoring for performance, availability, and usage; help implement model performance/drift monitoring pipelines. " Role Descriptions: Role SummaryWe are seeking a Python FastAPI Engineer to build and operate scalable API services for machine learningdriven risk models (e.g.| PDLGDrisk rating). The role will focus on real time scoring APIs| batch scoring pipelines| secure integration with data sources| and production readiness including monitoring| logging| and model governance. Key ResponsibilitiesDesign and develop REST APIs using Python FastAPI to serve real time model scoring for individual loanscustomers and support batch scoring workloads. Integrate APIs with upstreamdownstream systems and ensure strong input validation and error handling. Support model hosting execution for Python-based models and enable flexible deployment patterns Implement model operational capabilities versioninglineage| audit trails| reason codesexplainability hooks| and champion challenger support where needed. Buildenable observability structured logging| metrics| tracing| and monitoring for performance| availability| and usage help implement model performancedrift monitoring pipelines. Required Skills ExperienceStrong hands-on experience with Python and FastAPI (building production REST services).Experience serving or integrating ML models in production (risk scoring| classificationregression| or similar). Solid engineering practices unitintegration testing| Git-based workflows| CICD| and API documentation (OpenAPISwagger).Familiarity with model governance concepts version controllineage| monitoring| explainabilityreason codes| audit logs. Experience with AWS andor model execution environments such as SageMaker Essential Skills: Role SummaryWe are seeking a Python FastAPI Engineer to build and operate scalable API services for machine learningdriven risk models (e.g.| PDLGDrisk rating). The role will focus on real time scoring APIs| batch scoring pipelines| secure integration with data sources| and production readiness including monitoring| logging| and model governance. Key ResponsibilitiesDesign and develop REST APIs using Python FastAPI to serve real time model scoring for individual loanscustomers and support batch scoring workloads. Integrate APIs with upstreamdownstream systems and ensure strong input validation and error handling. Support model hosting execution for Python-based models and enable flexible deployment patterns Implement model operational capabilities versioninglineage| audit trails| reason codesexplainability hooks| and champion challenger support where needed. Buildenable observability structured logging| metrics| tracing| and monitoring for performance| availability| and usage help implement model performancedrift monitoring pipelines. Required Skills ExperienceStrong hands-on experience with Python and FastAPI (building production REST services).Experience serving or integrating ML models in production (risk scoring| classificationregression| or similar). Solid engineering practices unitintegration testing| Git-based workflows| CICD| and API documentation (OpenAPISwagger).Familiarity with model governance concepts version controllineage| monitoring| explainabilityreason codes| audit logs. Experience with AWS andor model execution environments such as SageMaker Desirable Skills: Keyword: Skills: Digital : Python~Digital : Application Programming Interface (API)~Digital : Machine Learning~Digital : Python for Data Science Experience Required: 8-10