Note: The job is a remote job and is open to candidates in USA. IBM Consulting helps leading companies advance their hybrid cloud and AI journeys through collaborative technology and client engagements. IBM is seeking a Senior AI Engineer to build, fine-tune, deploy, and operationalize agentic AI solutions on Azure, while collaborating directly with clients and enterprise teams to deliver secure, scalable, production-grade systems.
Responsibilities
- 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
- This role can be performed from anywhere in the United States
Skills
- 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
- Master's Degree
- 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
Benefits
- This role can be performed from anywhere in the United States
- Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.
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