Artificial Intelligence Engineer (Ref: 196989)

About the job

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 full documentation and handover to the client at end of engagement. No post-delivery support is included.

Requirements added by the job poster

• 5+ years of work experience with .NET APIs

• 3+ years of work experience with OpenAI API