Python Software Developer

Key Responsibilities 1. Agent Orchestration & System Architecture Agentic Frameworks: Architect the orchestration layer (Agent of Agents) that coordinates specialized AI workers (e.g., Controllership Agent, Treasury Agent), managing state, task routing, and error handling. Workflow Logic: Design the "Task Router & Intent Classifier" services that map user prompts to specific skills/playbooks, ensuring deterministic execution of critical financial and engineering workflows. Microservices Design: Build scalable, containerized microservices that handle high-concurrency requests for the LeverEDGE platform, ensuring low-latency responses for real-time market intelligence and design trade-off analysis. 2. Enterprise Integration & API Gateways Secure SAP Connectivity: Build the "Data Access Layer" (DAL) that serves as the secure gateway for AI agents to read/write to SAP S/4HANA and Ariba, enforcing strict validation logic before any transaction is committed. Scalable Data Lakehouse connectivity: Build a scalable, secure, observable connection layer to the central data platform ensuring all read transactions are authenticated, authorized, and properly audited. RAG Integration: Develop the backend logic to interface with Vector Databases (e.g., Weaviate) and Retrieval Models, enabling the "Knowledge Mining Chatbot" tosecurely query historical proposals and technical documents. External API Managers: Construct robust connectors for external data feeds (e.g., S&P Global, Orbis) to power the Supply Chain Risk Scoring engines or LeverEDGE component comparative analysis, handling rate limiting, caching, and data normalization. 3. Security, Performance & Deployment Defense-Grade Security: Implement "Gateway & Policy Guard" services that enforce Authentication, Role-Based Access Control (RBAC), and PII/ITAR redaction before data ever reaches an LLM. On-Premise Optimization: Engineer systems for strictly air-gapped or on-premise deployment, optimizing for efficient resource usage on local GPU clusters rather than infinite cloud scaling. Reliability Engineering: Implement comprehensive logging, tracing, monitoring, and observability mechanisms adhering to EDGE policies ensuring enterprise best practices are followed Technical Requirements Core Languages: Expert proficiency in Python (FastAPI/Django) for AI integration and Go or Java for high-performance microservices. Containerization & Orchestration: Deep experience with Docker and Kubernetes (k8s) for deploying scalable applications in on-premise environments. API Architecture: Strong background in designing RESTful APIs and gRPC services. Experience building API Gateways (e.g., Kong, NGINX) for traffic management and security. Database Management: Proficiency with Relational Databases (PostgreSQL) for transactional data and Vector Databases (Weaviate, Milvus) for semantic search applications. Integration Protocols: Familiarity with enterprise integration patterns and ERP protocols (OData, SOAP) is a strong plus. Professional Qualifications Experience: 5+ years of experience in Backend Engineering, with a focus on building distributed systems or platforms that serve ML/AI models in production. Collaboration: Proven track record of working with Data Scientists to productize models and Frontend Engineers to deliver seamless user experiences.