AI Engineer

Key Responsibilities: AI Agent Development & Autonomous Systems Develop AI/ML-powered autonomous agents using Azure AI Foundry, Azure Agent AI Service, LangGraph, AutoGen, and Semantic KernelDesign and implement multi-agent AI workflows with reasoning, goal-driven planning, and adaptive memoryUtilize Semantic Kernel for long-term memory, function calling, and RAG-based workflowsOrchestrate and fine-tune AI models in production environments using Azure AI FoundryLLM & AI Integration in Full-Stack Applications Deploy GPT-4, Phi-3, and Hugging Face models on Azure OpenAI for enterprise applicationsIntegrate LLM-based services into .NET and Node.js APIsDevelop AI-powered UIs using React.js, embedding natural language interfaces and AI copilotsImplement vector search and retrieval with Azure Cognitive Search, Pinecone, and FAISSFull-Stack AI Application Development Build backend AI APIs using .NET Core or Node.js integrated with Azure AI servicesDevelop interactive AI-driven frontends using React.js, TypeScript, and Microsoft Fluent UIImplement AI-assisted chatbots, copilots, and workflow automation in enterprise applicationsEnsure scalability, performance, and security of AI-powered web applicationsEnterprise AI & Microsoft Ecosystem Integration Build LLM-driven enterprise copilots for Microsoft 365, Teams, and Power PlatformDevelop AI-powered assistants and chatbots integrating with Microsoft Graph API & Azure AI StudioAutomate workflows using AI agents in Power Automate and Azure Logic AppsEnhance enterprise search and knowledge management using Azure Cognitive Search and vector databasesAI Performance Optimization & Responsible AI Optimize LLM token usage, reduce latency, and improve cost efficiencyImplement prompt engineering, retrieval caching, and fine-tuned model deploymentEnsure compliance with Azure AI Content Safety, Responsible AI principles, and enterprise AI governanceBuild monitoring and explainability tools to track LLM outputs and mitigate risksCollaboration & AI Strategy Collaborate with Data Scientists, Software Engineers, and Cloud Architects for AI-driven solutionsAdvocate for multi-agent AI engineering and AI-assisted application development best practicesStay updated with Azure AI innovations and enterprise AI trendsContribute to open-source AI projects and Microsoft AI research initiativesPreferred Qualifications: Microsoft AI Certifications (Azure AI Engineer Associate, AI-102, DP-100)Experience with multi-modal AI models, LLMOps, and Reinforcement Learning from Human Feedback (RLHF)Background in cognitive architectures, explainable AI (XAI), and enterprise AI governanceContributions to open-source AI frameworks (LangGraph, AutoGen, Semantic Kernel, Transformers) Key Responsibilities: AI Agent Development & Autonomous Systems Develop AI/ML-powered autonomous agents using Azure AI Foundry, Azure Agent AI Service, LangGraph, AutoGen, and Semantic KernelDesign and implement multi-agent AI workflows with reasoning, goal-driven planning, and adaptive memoryUtilize Semantic Kernel for long-term memory, function calling, and RAG-based workflowsOrchestrate and fine-tune AI models in production environments using Azure AI FoundryLLM & AI Integration in Full-Stack Applications Deploy GPT-4, Phi-3, and Hugging Face models on Azure OpenAI for enterprise applicationsIntegrate LLM-based services into .NET and Node.js APIsDevelop AI-powered UIs using React.js, embedding natural language interfaces and AI copilotsImplement vector search and retrieval with Azure Cognitive Search, Pinecone, and FAISSFull-Stack AI Application Development Build backend AI APIs using .NET Core or Node.js integrated with Azure AI servicesDevelop interactive AI-driven frontends using React.js, TypeScript, and Microsoft Fluent UIImplement AI-assisted chatbots, copilots, and workflow automation in enterprise applicationsEnsure scalability, performance, and security of AI-powered web applicationsEnterprise AI & Microsoft Ecosystem Integration Build LLM-driven enterprise copilots for Microsoft 365, Teams, and Power PlatformDevelop AI-powered assistants and chatbots integrating with Microsoft Graph API & Azure AI StudioAutomate workflows using AI agents in Power Automate and Azure Logic AppsEnhance enterprise search and knowledge management using Azure Cognitive Search and vector databasesAI Performance Optimization & Responsible AI Optimize LLM token usage, reduce latency, and improve cost efficiencyImplement prompt engineering, retrieval caching, and fine-tuned model deploymentEnsure compliance with Azure AI Content Safety, Responsible AI principles, and enterprise AI governanceBuild monitoring and explainability tools to track LLM outputs and mitigate risksCollaboration & AI Strategy Collaborate with Data Scientists, Software Engineers, and Cloud Architects for AI-driven solutionsAdvocate for multi-agent AI engineering and AI-assisted application development best practicesStay updated with Azure AI innovations and enterprise