Artificial Intelligence Engineer (Ref: 196989)

Role purpose

Build and harden the AI layer of a customer-facing agentic-AI solution (two AI agents) and migrate it from PoV to production on the client’s .NET-based architecture . Hands-on delivery role focused on LLM integration, agent orchestration, retrieval pipelines, Responsible AI, and production readiness — working alongside backend and DevOps engineers to deliver a complete, production-grade system.

Key responsibilities

• Design and implement AI services in .NET / C# , integrating LLM providers (Azure OpenAI / equivalent) into the application architecture.
• Build and orchestrate agentic AI workflows (multi-agent / tool-using systems), including prompt design, context handling, and tool/API invocation.
• Re-platform PoV AI services, prompts, and retrieval pipelines onto the client’s approved .NET architecture (migration execution).
• Implement RAG (Retrieval-Augmented Generation) pipelines: document ingestion, embeddings, vector search, grounding, and response citation.
• Replace PoV mock AI/data interactions with production-ready integrations, working with backend teams on Oracle Fusion , JD Edwards , and other enterprise APIs.
• Implement and harden Responsible AI guardrails (content filtering, prompt protection, output validation, safety policies) for public-facing use.
• Build prompt templates, evaluation datasets, and AI testing frameworks to ensure response quality, accuracy, and consistency.
• Optimise AI performance (latency, cost, caching strategies, retry patterns) and ensure scalability and load readiness for production traffic.
• Contribute to security and compliance , including handling of sensitive data, access control context, and enterprise governance standards.
• Support channel integration (web, WhatsApp where applicable) and ensure AI behaviour aligns with user journeys (registration, onboarding, country restrictions).
• Implement logging, monitoring, and observability for AI services (prompt/response tracing, failure analysis, usage metrics).
• Collaborate with .NET engineers, DevOps, and QA to deliver a fully integrated production solution and document for client handover.

Required skills & experience

• 5+ years building production software in .NET / C# / ASP.NET Core .
• Hands-on experience integrating LLM / GenAI solutions into applications (chatbots, copilots, agent-based systems).
• Strong experience with Azure OpenAI / OpenAI APIs (or equivalent LLM platforms).
• Experience with RAG architectures , vector databases, embeddings, and semantic search.
• Familiarity with agent frameworks (e.g. Semantic Kernel, LangChain, AutoGen, or similar), preferably in .NET environments.
• Solid understanding of prompt engineering, evaluation, and AI testing practices.
• Experience implementing Responsible AI / AI safety controls in production systems.
• Strong API integration experience (REST, OAuth2, enterprise systems).
• Experience working in enterprise / regulated environments and delivering production-grade systems.
• Git, testing practices, clean architecture; strong English communication.

Nice to have

• Microsoft Certified: Azure AI Engineer Associate (or similar).
• Experience with Semantic Kernel / Microsoft AI stack in .NET.
• Prior integration with Oracle Fusion, JD Edwards , or similar ERP/HCM systems.
• WhatsApp Business API or multi-channel conversational interfaces.
• Experience in public-facing AI assistants or recruitment / onboarding use cases.

Deliverables / success criteria

Production-ready AI layer deployed on the client’s .NET architecture, including LLM integration, agent orchestration, and RAG pipelines; backend integrations aligned; Responsible AI guardrails implemented; performance, scalability and security targets met; and