Agentic AI Architect - Hybrid (Dallas or Houston TX)

AECOM • Dallas, TX

16h • Hybrid

About The Position

We are seeking a hands-on Agentic AI Architect with strong expertise in Agentic Workflows, MCP, C#, SQL Server, Angular, and Azure Cloud technologies to architect, design, build, and optimize enterprise-grade AI platforms. The ideal candidate will have hands-on experience with modern AI frameworks , including Agentic Workflows, MCP, Semantic Kernel, Kernel Memory, Azure AI Foundry , and the integration of multiple LLMs in advanced architecture such as Retrieval Augmented Generation (RAG) . This role requires a balance of advanced AI architecture experience, hands-on full-stack development skills with a forward-looking mindset on AI-driven platform development , ensuring scalability, security, and performance in production systems. We are offering an opportunity to work with cutting-edge AI and cloud technologies . This position offers flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work, to be based in Houston, TX or Dallas, TX.

Requirements

• BA/BS plus at least 10 years of professional architecture experience in full-stack platforms or demonstrated equivalency of experience and/or education
• Hands-on architect abilities to collaborate with current Agile SCRUM team.
• Ability to work with little guidance on POCs and forward-looking research on upcoming AI technologies.
• Strong front-end experience with Angular (latest versions preferred).
• Expertise in SQL Server including complex queries, performance tuning, and data modeling.
• Hands-on experience with Azure Cloud services including monitoring and troubleshooting Cloud issues .
• Practical experience with Agentic Workflows, AI Agents, MCP, API Management (APIM) , Semantic Kernel, Kernel Memory, AI Foundry , or similar AI frameworks.
• Familiarity with LLM integration and Retrieval Augmented Generation (RAG) architectures.
• Strong understanding of software engineering principles , design patterns, and platform architecture.

Nice To Haves

• Experience with architecting AI platforms.
• Observability, alerting and scaling applications with more than 50K users.
• Knowledge of containerization (Docker), DevOps practices (CI/CD pipelines in Azure DevOps/GitHub Actions).
• Prior work on AI-driven platforms in production environments including chatbots.
• Background in machine learning concepts (vector databases, embeddings, prompt engineering).
• Exposure to multimodal AI capabilities such as: Voice-to-Text (speech recognition) Text-to-Speech (natural voice generation) Image-to-Text (OCR, vision-to-language) Other modalities (audio, video, and sensor data AI applications).
• Can translate user story requirements into architecture implementation direction for the engineering team.
• Problem solver with a practical mindset and ability to balance innovation with execution.
• Strong collaborator and communicator, able to explain complex technical concepts to both technical and non-technical stakeholders.
• Proactive and self-driven , thriving in fast-paced environments.
• Passionate about AI adoption and cloud-first architecture .
• Shares the Agile mindset of the team and is willing and open to sharing ideas, concepts and perspectives with the team.

Responsibilities

• Platform Architecture & Technical Leadership Define and evolve the enterprise AI platform architecture, breaking down business and functional requirements into scalable, secure, and maintainable technical designs.
• Establish the long-term architecture runway for AI products, owning POCs, design spikes, and forward-looking evaluations of emerging technologies— including agentic architectures, orchestration frameworks, and tool-use patterns .
• Apply expertise in distributed systems and API design to ensure the platform is robust, observable, and simple to extend.
• Ensure best practices in secure coding, performance optimization, resilience, and lifecycle maintainability.
• AI, Agentic Workflows & Cloud Integration Architect and implement enterprise-grade RAG (Retrieval Augmented Generation) pipelines , including vector search, embeddings, knowledge stores, and LLM orchestration.
• Design and operationalize agentic workflows , including multi-agent collaboration, tool-calling agents, planner/executor patterns, and automated reasoning loops.
• Build and integrate production-grade AI agents capable of interacting with external tools, APIs, enterprise systems, and knowledge sources.
• Utilize the Model Context Protocol (MCP) to expose internal tools, datasets, and enterprise APIs to LLMs in a secure, governed manner.
• Integrate and operationalize multiple LLM providers (Azure OpenAI, OpenAI, Anthropic, open-source models) into production systems.
• Leverage Azure AI services— Semantic Kernel , Kernel Memory , AI Foundry , Cognitive Search—to enable intelligent, context-aware platform capabilities.
• Design, deploy, and optimize cloud-native AI applications in Azure with emphasis on cost efficiency, observability, resilience, and security.
• Cross-Functional Collaboration & Mentorship Partners with enterprise architects, product managers, developers, and data engineers in an Agile/Scrum environment to ensure architecture aligns with product strategy.
• Provide hands-on mentorship on AI development patterns—including agent-building, RAG troubleshooting, prompt design, vector search patterns, and orchestration frameworks.
• Conduct architecture reviews, code reviews, and design sessions to drive engineering quality and consistency.
• Translate business needs into scalable platform capabilities that support long-term product and enterprise roadmaps.
• Innovation, Governance & Continuous Improvement Stay current on rapidly evolving AI trends: multi-agent systems, model orchestration, tool-use protocols, evaluation frameworks, prompt engineering, latency optimization, and AI safety approaches .
• Recommend improvements to development, testing, deployment, and estimation processes to increase delivery velocity while maintaining quality and compliance.
• Establish platform-level governance patterns including architectural guardrails, reusable modules, MCP tool adapters, and standardized agent templates.
• Champion experimentation, rapid prototyping, and continuous adoption of modern AI engineering practices to increase the organization’s AI maturity.

Benefits

• AECOM benefits may include medical, dental, vision, life, AD&D, disability benefits, paid time off, leaves of absences, voluntary benefits, perks, flexible work options , well-being resources, employee assistance program, business travel insurance, service recognition awards, retirement savings plan, and employee stock purchase plan.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 5,001-10,000 employees