- 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" to securely 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.

- Structured Delivery : Ability to thrive in a "Governance Collision" environment delivering Agile software (Sprints, MVPs) that passes rigorous "Stage Gate" reviews and Systems Engineering audits.

- Operational Mindset : Experience building systems that require high availability and auditability, preferably in Fintech, Healthcare, or Defense sectors.

- Collaboration : Proven track record of working with Data Scientists to productize models and Frontend Engineers to deliver seamless user experiences.