Procode Developer (GenAI / LLM Engineer)
Procode Developer (GenAI / LLM Engineer) Role Overview The Procode Developer (GenAI / LLM Engineer) will play a critical role in designing, building, and delivering enterprise-grade Generative AI solutions on Microsoft Azure. This role is focused on translating business use cases into secure, scalable, and production-ready AI systems that deliver measurable business value while meeting enterprise governance, security, and compliance standards. Key Responsibilities Enterprise AI Solution Delivery • Design and implement GenAI solutions using Azure OpenAI and Azure AI Foundry • Build retrieval-augmented generation (RAG) architectures • Develop agentic AI systems for enterprise use cases • Implement prompt engineering, orchestration, and tool/function calling Platform & Architecture Enablement • Design cloud-native, scalable AI architectures on Azure • Integrate AI services with enterprise systems and data platforms • Support modernisation and AI platform enablement initiatives Governance, Security & Compliance • Implement responsible AI (RAI) controls and safety guardrails • Align solutions with enterprise security, compliance, and governance frameworks • Support model evaluation, monitoring, and auditability Delivery & Transformation Support • Work with client stakeholders across business, IT, and leadership • Support agile delivery models and enterprise programs • Enable knowledge transfer, documentation, and internal capability building Core Capabilities Provided • GenAI platform engineering • LLM application development • Enterprise AI architecture • Agentic system design • AI governance & safety • Cloud-native delivery • DevOps & MLOps enablement Technology Landscape • Languages: Python • AI Platforms: Azure OpenAI, Azure AI Foundry • Azure Services: AI Search, Functions, Key Vault, Event Grid, Service Bus, Storage, App Service, Container Apps • Frameworks: Semantic Kernel, LangGraph, AutoGen • DevOps: Git, CI/CD, Observability (App Insights, Log Analytics) • Data: Vector DBs, embeddings, RAG pipelines Engagement Outcomes • Production-ready AI solutions • Secure enterprise AI platform • Scalable AI architecture • Measurable business value • Accelerated AI adoption • Sustainable internal capability Apply tot his job