Senior Backend Executive

Option Us

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" 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" environmentdelivering 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.