Procode Developer (GenAI / LLM Engineer)

🌍 Remote, USA 🎯 Full-time πŸ• Posted Recently

Job Description

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

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