Build Secure Agentic AI Integration Layer for Enterprise Risk Management Platform
Budget: ₹1,500 – ₹12,500 INR
We are looking for an experienced AI/Backend Engineer (or small team) to help design and implement a foundational setup for an enterprise “Agentic AI” assistant.The goal is to enable a prompt-based interface where business users can ask questions and trigger actions across internal risk management and document systems through a governed API layer.
This is NOT a chatbot UI project.This project focuses on backend architecture, AI orchestration, and secure enterprise integration.
Target application includes IBM OpenPages SaaS (GRC) accessed via REST APIs.
The first phase will implement a proof-of-concept that allows an AI agent to retrieve and update structured records using approved APIs via a secure middleware service.
Scope of Work (Phase-1: Foundational Setup)
Build a secure middleware API service (AI Gateway)
Integrate an enterprise LLM (Azure OpenAI or equivalent hosted LLM)
Implement tool/function calling (agent actions)
Connect to an existing enterprise system via REST APIs (query + update operations)
Implement structured response schema (business entities like risks, issues, controls, tasks)
Implement logging, traceability, and error handling
Implement role-aware request handling (no hardcoded credentials)
Ensure deployment architecture supports IP allow-listing
Provide clear documentation and deployment instructions
No UI required (basic Postman/test interface sufficient)
Expected Deliverables
Backend AI orchestration service (production-style codebase)
Tool schemas and function calling implementation
API mapping layer (business model → system APIs)
Sample prompts and evaluation tests
Security design notes
Deployment guide (cloud-ready)
Architecture diagram
Required Technical Skills
Core Backend & APIs
Python (FastAPI / Flask) OR Node.js backend development
REST API integration and schema mapping
OAuth / token-based authentication handling
Secure error handling and structured logging
AI / LLM
OpenAI / Azure OpenAI APIs
Function calling / tool calling agents
Prompt engineering for structured outputs
Retrieval-augmented workflows (optional but preferred)
Cloud & Infrastructure
Azure (preferred) or AWS/GCP deployment
Static outbound IP / networking configuration
Containerization (Docker)
Environment configuration management
Architecture Knowledge
Designing middleware integration layers
Converting complex APIs into business semantic APIs
Handling long-running workflows
Designing safe update operations (write vs read actions)
Nice to Have
Experience in enterprise systems integration
Experience with agent frameworks (LangChain, LangGraph, Semantic Kernel, or similar)
Knowledge of approval workflows / audit logging
Experience deploying internal copilots or assistants
Responsibilities
Design the AI orchestration pattern (not just code implementation)
Implement secure tool-based action execution
Prevent unsafe or unintended write operations
Provide extensible architecture for future integrations
Document how new tools/actions can be added later
Ensure solution is maintainable by internal developers
Engagement TypeShort initial phase (2–4 weeks) with potential long-term extension for full platform buildout.
When Applying, Please Include
Relevant projects involving LLM integrations or AI agents
Example of tool/function calling implementation (code or architecture)
Cloud deployment experience
Preferred tech stack and why
Estimated timeline for Phase-1
We are specifically looking for someone who understands enterprise-grade AI integration, not only prompt engineering or UI chatbot development
This is NOT a chatbot UI project.This project focuses on backend architecture, AI orchestration, and secure enterprise integration.
Target application includes IBM OpenPages SaaS (GRC) accessed via REST APIs.
The first phase will implement a proof-of-concept that allows an AI agent to retrieve and update structured records using approved APIs via a secure middleware service.
Scope of Work (Phase-1: Foundational Setup)
Build a secure middleware API service (AI Gateway)
Integrate an enterprise LLM (Azure OpenAI or equivalent hosted LLM)
Implement tool/function calling (agent actions)
Connect to an existing enterprise system via REST APIs (query + update operations)
Implement structured response schema (business entities like risks, issues, controls, tasks)
Implement logging, traceability, and error handling
Implement role-aware request handling (no hardcoded credentials)
Ensure deployment architecture supports IP allow-listing
Provide clear documentation and deployment instructions
No UI required (basic Postman/test interface sufficient)
Expected Deliverables
Backend AI orchestration service (production-style codebase)
Tool schemas and function calling implementation
API mapping layer (business model → system APIs)
Sample prompts and evaluation tests
Security design notes
Deployment guide (cloud-ready)
Architecture diagram
Required Technical Skills
Core Backend & APIs
Python (FastAPI / Flask) OR Node.js backend development
REST API integration and schema mapping
OAuth / token-based authentication handling
Secure error handling and structured logging
AI / LLM
OpenAI / Azure OpenAI APIs
Function calling / tool calling agents
Prompt engineering for structured outputs
Retrieval-augmented workflows (optional but preferred)
Cloud & Infrastructure
Azure (preferred) or AWS/GCP deployment
Static outbound IP / networking configuration
Containerization (Docker)
Environment configuration management
Architecture Knowledge
Designing middleware integration layers
Converting complex APIs into business semantic APIs
Handling long-running workflows
Designing safe update operations (write vs read actions)
Nice to Have
Experience in enterprise systems integration
Experience with agent frameworks (LangChain, LangGraph, Semantic Kernel, or similar)
Knowledge of approval workflows / audit logging
Experience deploying internal copilots or assistants
Responsibilities
Design the AI orchestration pattern (not just code implementation)
Implement secure tool-based action execution
Prevent unsafe or unintended write operations
Provide extensible architecture for future integrations
Document how new tools/actions can be added later
Ensure solution is maintainable by internal developers
Engagement TypeShort initial phase (2–4 weeks) with potential long-term extension for full platform buildout.
When Applying, Please Include
Relevant projects involving LLM integrations or AI agents
Example of tool/function calling implementation (code or architecture)
Cloud deployment experience
Preferred tech stack and why
Estimated timeline for Phase-1
We are specifically looking for someone who understands enterprise-grade AI integration, not only prompt engineering or UI chatbot development