You'll be joining Neurons Lab as a Python Backend Developer to contribute to the development of a cutting-edge healthcare SaaS platform. This is a GenAI-powered project in the healthcare domain where you'll work alongside an AI Architect to build production-ready systems for processing medical documents, classifying medical device incidents, and delivering automated monitoring reports.

As a Software Engineer, you'll be responsible for implementing robust APIs, working with graph databases, integrating AI/GenAI pipelines, and ensuring the platform meets strict compliance and auditability requirements for healthcare clients. You'll focus on code quality, testing, and day-to-day delivery while the AI Architect leads the technical vision.

This role requires strong expertise in Python backend development, graph databases, API design, and a willingness to work with GenAI technologies. Experience in healthcare or regulated industries is a significant advantage.

Duration: 2-month project engagement

Objective

Backend API Development: Build and maintain production-ready REST APIs using FastAPI with proper authentication, authorization, and error handling
Graph Database Implementation: Implement and optimize Memgraph database models, Cypher queries, and knowledge graph structures for medical data
AI/GenAI Integration: Integrate backend services with AI engines for document processing, entity extraction, and classification
Multi-Tenant SaaS Architecture: Develop tenant-aware data models, queries, and APIs ensuring complete data isolation between organizations
Audit Trail & Compliance: Implement comprehensive audit logging and provenance tracking for all user and AI decisions to meet healthcare regulatory requirements
Workflow Integration: Connect backend services with Airflow pipelines for document processing and batch operations

KPI

Code Quality:

Maintain 80%+ test coverage for all backend code
Zero critical bugs in production releases
Pass all code reviews with minimal revision cycles

API Reliability:

99%+ API uptime during business hours
API response times under 2000ms for standard operations
Comprehensive API documentation kept up-to-date

Compliance & Documentation:

100% of user and AI actions captured in audit trails
Complete technical documentation for all implemented features
Knowledge transfer documentation for system maintenance

Areas of Responsibility

Backend Development (50%):

Design and implement REST APIs using FastAPI with Pydantic models
Develop business logic for medical document processing workflows
Implement authentication, authorization, and multi-tenant data isolation
Build tenant-aware API endpoints for SaaS platform features

Graph Database & Data Layer (25%):

Design graph data models and write optimized Cypher queries
Implement ontology structures (RDF, OWL) and provenance tracking (PROV)
Build data validation and integrity checks for graph operations

Integration & Workflows (15%):

Connect backend services with Airflow DAGs for document processing
Develop APIs to invoke AI engines and handle processing results
Integrate with external data sources and third-party services

Quality & Compliance (10%):

Write unit and integration tests using pytest
Build comprehensive audit logging for all user and AI actions
Document APIs and maintain coding standards

Skills

Required:

Python: Advanced proficiency in Python 3.9+, FastAPI, Pydantic, async programming
Graph Databases: Hands-on experience with Memgraph or Neo4j, Cypher query language
SQL Databases: PostgreSQL experience for relational data storage
RESTful API Design: Strong understanding of REST principles, API versioning, error handling
Testing: pytest, unit testing, integration testing, mocking frameworks
AI Code Generation Tools: Proficiency with Claude Code, Cursor, or similar
Development Tools: Git, Docker, Linux/UNIX command line

Strong Plus:

Workflow Orchestration: Airflow experience for pipeline development
GenAI/LLM Integration: Experience integrating LLM APIs, RAG systems, or document processing AI
Multi-Tenant SaaS: Experience building tenant-isolated architectures
Ontology Standards:
RDF (Resource Description Framework) - standard model for data interchange
OWL (Web Ontology Language) - semantic markup for ontologies
PROV Ontology: W3C Provenance standard for tracking data lineage and history
Healthcare Domain: Experience with regulated industries
React/Frontend: Full-stack capability with React for UI development (nice to have for occasional frontend contributions)