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
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