Artificial Intelligence Engineer (Ref: 196989)
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Key skills for this role
About the Role
Forsyth Barnes is seeking an AI Engineer to build and harden the AI layer of a customer-facing agentic-AI solution, migrating it from PoV to production on a.NET-based architecture.
Key Skills for This Role
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.
Requirements
- 5+ years building production software in .NET / C# / ASP.NET Core
- Hands on experience integrating LLM / GenAI solutions into applications
- Strong experience with Azure OpenAI / OpenAI APIs
- Experience with RAG architectures, vector databases, embeddings, and semantic search
- Familiarity with agent frameworks (e.g. Semantic Kernel, LangChain, AutoGen)
- 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
Full Job Posting
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
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