Senior Application Engineer with Python and FastApi

The Senior Application Engineer will augment the project team to investigate and resolve defects across backend services, n8n workflows, database operations, and enterprise integrations. The role requires hands-on expertise in Python, FastAPI, n8n, PostgreSQL, and containerized applications on Microsoft Azure.

The engineer will support the application throughout development (DEV), System Integration Testing (SIT), User Acceptance Testing (UAT), production (PROD), and post-production monitoring. The role will collaborate with development, QA, business, infrastructure, and support teams to deliver tested fixes, support controlled releases, and maintain reliable application and workflow execution.

Responsibilities

Investigate, reproduce, and resolve defects in FastAPI services, business decision rules, PostgreSQL operations, and integrations with the Loan Origination System (LOS) and other enterprise services.

Develop, maintain, and troubleshoot n8n workflows, including triggers, webhooks, API calls, expressions, JavaScript code nodes, data transformations, conditional routing, and reusable sub-workflows.

Resolve workflow issues involving sequencing, retries, timeouts, duplicate processing, error handling, and human-in-the-loop approval and resumption.

Perform root cause analysis using application logs, n8n execution histories, API responses, and database queries; document findings, corrective actions, and validation evidence.

Implement and validate fixes in DEV; support SIT integration testing and UAT business validation, including defect triage, retesting, and regression testing.

Support promotion of application code, n8n workflows, database migrations, and environment-specific configurations across DEV, SIT, UAT, and PROD, following approved release and change-management procedures.

Support Docker image builds, Azure Container Registry (ACR) image management, deployment troubleshooting, production smoke testing, and rollback activities in coordination with infrastructure teams.

Conduct post-production monitoring and hypercare, reviewing workflow failures, API errors, processing delays, retry behavior, and application performance; investigate incidents and verify recovery.

Maintain technical documentation, defect records, release notes, and operational runbooks; provide knowledge transfer to BPI’s development and support teams.

Bachelors degree in Computer Science, Information Technology, Engineering, or a related technical field.

At least 5 years of software engineering experience, preferably five to seven years, with substantial experience maintaining enterprise applications and resolving defects.

Strong proficiency in Python and FastAPI, including asynchronous programming, REST APIs, Pydantic validation, exception handling, and enterprise API integration.

Hands-on n8n development and troubleshooting experience, including workflow design, node configuration, expressions, JavaScript, JSON processing, credentials, webhooks, sub-workflows, and execution debugging.

Experience implementing reliable workflow behavior through error handling, retries, idempotency, and recovery or resumption of interrupted processing.

Strong SQL and PostgreSQL skills, including JSON/JSONB, data-integrity investigation, SQLAlchemy, and database migrations.

Demonstrated experience working across DEV, SIT, UAT, and PROD environments, including configuration management, release promotion, testing support, deployment validation, and rollback procedures.

Experience with production support, post-release hypercare, log monitoring, alert investigation, incident triage, and root cause analysis.

Familiarity with Docker, Microsoft Azure, ACR, Git, CI/CD practices, and automated testing using pytest.

Strong analytical, communication, and collaboration skills; banking, financial services, or loan-processing experience is an advantage.

Experience with Azure Blob Storage, Service Bus, Document Intelligence, and Azure OpenAI integrations is an advantage.

Expected Deliverables

Reviewed defect fixes with root cause findings, regression coverage, and SIT/UAT retesting evidence.

Validated releases with documented configuration changes, deployment steps, and rollback instructions.

Post-production monitoring updates, incident findings, knowledge transfer, and post-mortems