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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic AI Platform / Solution Architect - **Company:** Thunderhawk Technology Partners - **Location:** Chicago, IL, United States - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, C Sharp (Programming Language), Cloud Computing, Data Masking, Memory Management, Graph Database, Monitoring of Systems, Python (Programming Language), Knowledge Management, Knowledge-Based Systems, PostgreSQL, MongoDB, Neo4j, Queueing Systems, Software Engineering, SQL Databases, Data Streaming, Usage Analysis, Azure Service Bus, Data Logging, Data Storage Management, Google Cloud, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Rate Limiting, Usage Tracking, Event Driven Architecture, Build Management, AI Platforms, Low Latency, Enterprise Integration, Apache Kafka, Cosmos DB, Machine Learning Operations, Virtual Agents, Api Design, Api Gateway, Restful APIs, Automation Anywhere, Api Management - **Published:** August 28, 2026 - **Apply:** https://www.dice.com/job-detail/3604d417-9623-4108-bac0-b7190b1f8f07 ## About the Role We are seeking a highly skilled Senior AI Engineer to help design and build an enterprise-scale Agentic AI platform that enables multiple business domains to develop, deploy, monitor, and govern autonomous AI agents. This is an architecture-focused AI engineering role requiring hands-on expertise in agent orchestration, AI platform architecture, model governance, memory management, observability, cost attribution, multi-agent systems, and scalable cloud-native AI solutions. The ideal candidate will have experience building production-grade AI systems using Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, vector databases, API gateways, and modern AI engineering practices., * 7+ years of software engineering or platform engineering experience. * 3+ years building AI/ML or Generative AI solutions. * Experience delivering enterprise-scale production AI applications. * Experience designing AI architectures and platforms, not just individual AI applications. * Strong hands-on experience with AI Agents / Agentic AI. * Python - required. * Microsoft Azure - required. * Experience with: + Azure AI Foundry + Azure OpenAI + LangChain + LangGraph + MCP (Model Context Protocol) * Strong understanding of multi-agent orchestration patterns. * Experience with AI platform governance, observability, and cost management. * Experience with Azure API Management (APIM) and REST APIs. * Strong understanding of event-driven systems. * Experience with vector databases and RAG architectures. * Strong understanding of AI memory and knowledge management. * Experience with AI monitoring, logging, token usage analysis, and cost optimization. Technical Skills Programming: Python, SQL; C#/.NET preferred Cloud: Microsoft Azure required; Google Cloud Platform/AWS is a plus AI/ML & Agentic AI: Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, Semantic Kernel, MCP Enterprise Integration: Azure APIM, REST APIs, AI gateways, event-driven architectures Data & Storage: Cosmos DB, PostgreSQL, MongoDB, Vector Databases, Graph Databases Graph Technologies: Neo4j, Stardog, Neptune, or similar Messaging & Streaming: Kafka, Azure Service Bus, Event Grid, Durable Functions AI Operations: AI observability, monitoring/logging, token usage analysis, cost optimization, model lifecycle management Preferred / Desirable Skills * Experience implementing ontology-driven AI solutions. * Experience with enterprise knowledge graphs. * Experience building autonomous AI systems. * Experience with AI governance and Responsible AI frameworks. * Experience designing reusable AI platforms consumed by multiple business units. * Experience in healthcare, financial services, insurance, or other regulated industries. * Strong architecture and technical leadership capabilities. Architecture Focus - Important This is not a traditional LLM application-development role. Candidates should be able to discuss and demonstrate practical experience with: * Architecture trade-offs * Agent orchestration patterns * Choreography vs. orchestration * AI memory management strategies * Graph databases and ontology * AI platform governance * APIM and AI gateway patterns * Closed-loop AI evaluation * Harm/risk/context engineering * Cost attribution * Multi-tenant AI platforms * Enterprise Agentic AI architecture The ideal candidate should be capable of making architecture decisions, evaluating technology trade-offs, and designing secure, scalable, observable, and governed enterprise AI platforms. Preferred title alignment: Senior AI Platform Engineer - Agentic AI / Agentic AI Solutions Architect ## Description <>Agentic AI Solution Development * Design and develop sophisticated multi-agent AI systems for enterprise use cases. * Build autonomous and semi-autonomous AI workflows using Agentic AI patterns. * Implement supervisor-worker, sequential, orchestration, choreography, ReAct, Planner-Executor, and Writer-Critic architectures. * Develop scalable agent communication and execution frameworks. * Design closed-loop AI workflows with validation, retry, evaluation, and feedback mechanisms. <>Enterprise AI Platform Engineering * Build reusable AI platform capabilities consumed by multiple business teams. * Implement enterprise-grade AI governance and operational controls. * Design API-driven AI service architectures supporting: + Rate limiting + Quota management + Multi-tenant usage tracking + Cost attribution + Authentication & authorization + Audit logging * Enable structured onboarding and lifecycle management of AI agents. <>Multi-Agent Orchestration * Design agent communication using direct calls, event-driven architectures, message queues, and publish-subscribe patterns. * Implement choreography and conductor-based execution models. * Evaluate and integrate technologies such as Kafka, Azure Durable Functions, Azure Service Bus, and event-driven workflows. <>AI Memory & Knowledge Systems * Design short-term and long-term AI memory architectures. * Implement vector databases, semantic caching, conversation memory, agent-state persistence, and RAG. * Develop knowledge orchestration frameworks supporting agent collaboration. <>Ontology & Graph-Based Intelligence * Work with graph databases and enterprise knowledge models. * Support ontology-driven AI applications. * Build knowledge graphs enabling relationship-based reasoning and signal generation. * Combine structured, unstructured, and graph-based knowledge sources. <>Model Governance & FinOps * Implement AI consumption governance across business domains. * Track token usage, model consumption, API utilization, and operational costs. * Create chargeback/showback mechanisms for enterprise teams. * Support AI FinOps reporting and capacity planning. <>Reliability, Monitoring & Observability * Design observability frameworks for AI applications. * Monitor agent executions, tool usage, latency, hallucinations, failure rates, and model quality. * Create dashboards and operational metrics for enterprise AI workloads. <>Responsible AI & Security * Implement guardrails, safety controls, prompt protection, data masking, PII protection, and human-in-the-loop validation. * Ensure compliance with enterprise security and governance policies. * Build secure agentic systems handling sensitive business data. <>AI Evaluation & Optimization * Develop frameworks for agent evaluation, tool evaluation, response quality measurement, closed-loop evaluation, and hallucination detection. * Apply advanced AI engineering techniques including: + Context engineering + Prompt engineering + Retrieval optimization + Agent tuning + AI system benchmarking ## Related Videos - [Watch Tests Go Brrrr! 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