Senior AI Engineer - Agentic Systems

Role Overview

We are seeking a skilled AI Engineer to join an active enterprise AI engagement in a hands-on, client-facing capacity. This is a senior individual contributor role requiring deep technical proficiency in Agentic AI.

What You'll Do

• Agentic AI Development
• Design, build, and deploy agentic AI workflows and multi-agent systems using Azure OpenAI and supporting frameworks
• Develop and optimize prompt engineering strategies to maximize model performance and reliability
• Build and maintain Retrieval-Augmented Generation (RAG) pipelines that enable grounded, context-aware AI responses
• Implement agent orchestration patterns including tool calling, memory management, and human-in-the-loop workflows
• Engage directly with client stakeholders to gather requirements, demo capabilities, and walk through technical solutions
• Communicate clearly on progress, blockers, and technical tradeoffs — no hand-holding required
• Collaborate with architects, data engineers, and business teams to ensure seamless end-to-end integration
• Deploy and manage AI models using Azure ML, Azure AI Studio, or equivalent Azure services
• Integrate AI capabilities into enterprise applications via APIs, event-driven pipelines, and microservices
• Establish monitoring, evaluation, and feedback loops to ensure ongoing model performance and reliability in production
• Ensure solutions meet enterprise standards for security, scalability, and compliance

Preferred Education and Technical Expertise

• Master's Degree Required
• 7+ years designing, developing, and deploying scalable AI applications leveraging LLMs, RAG architectures, and agentic AI workflows
• Build and operationalize AI orchestration pipelines using frameworks such as LangChain and LangGraph
• Develop AI agents capable of tool calling, contextual retrieval, memory/state management, multi-agent coordination, and autonomous workflow execution
• Implement MCP (Model Context Protocol) integration patterns to enable secure, modular interoperability between AI agents, enterprise systems, tools, and data sources
• Support the industrialization of AI capabilities through reusable architecture patterns, standardized deployment frameworks, monitoring, testing, evaluation pipelines, and operational support models
• Develop and integrate enterprise-grade APIs, vector databases, workflow platforms, and operational systems into AI-enabled business processes
• Implement AI governance, security, logging, guardrails, and human-in-the-loop controls to support responsible and scalable AI adoption
• Contribute to CI/CD, LLMOps/MLOps, and cloud-native deployment practices supporting enterprise-scale AI delivery

Preferred Skills

• Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, Semantic Kernel, or CrewAI
• Familiarity with vector databases (e.g., Azure AI Search, Pinecone, Weaviate) for RAG implementations
• Knowledge of MLOps practices and CI/CD pipelines for AI model deployment and lifecycle management
• Experience with enterprise integration patterns and connecting AI solutions to CRMs, ERPs, or data platforms