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Agentic AI Solution Architect

Agentic AI Solution Architect

Select Minds LLCDallas, TX, US
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Job Description

Job Description

Benefits :

  • Oppurtunity for Advancement
  • Hybrid
  • Long Term

Job Title : Agentic AI Solution Architect

Location : Dallas, TX (Day 1 ONSITE)

Interview : Inperson

Department : AI Delivery

Role Summary :

You will design, architect, and lead the deployment of agentic (autonomous, multi-agent) AI systems across the Client's Operations, Systems, and Customer-facing domains. You'll bridge business goals, existing infrastructure, and nextgen AI capabilities to deliver high-impact solutions aligned with Client's strategy.

Key Responsibilities -

  • Define end-to-end architecture for agentic AI systems (multi-agent orchestration, coordination, planning, feedback loops) - Evaluate, select, and integrate AI frameworks, platforms, and infrastructure components.
  • Collaborate with business units (operations, crew scheduling, customer service, disruption management) to map use cases to agentic AI designs.
  • Define interfaces, APIs, data flows, governance and security models for agents to interact with legacy systems.
  • Establish architecture patterns, best practices, design principles for autonomous agents in an enterprise setting.
  • Lead technical reviews, prototype validation, and iteration cycles.
  • Mentor / coach engineering and AI teams on agentic design and scalable AI deployment.
  • Stay abreast of research in autonomous agents, reinforcement learning, selfadaptation, and inject innovations into Client's roadmap.
  • Requirements / Must-Haves

  • Bachelor's or Master's in Computer Science, AI, Systems Engineering, or related.
  • Strong background in AI / ML, especially reinforcement learning, multi-agent systems, planning, feedback control.
  • Proven experience in solution architecture for large-scale distributed systems, microservices, event-driven / streaming architecture. Experience building or integrating autonomous or semi-autonomous agents or intelligent systems - Proficiency with modern cloud platforms (AWS, Azure, GCP) and AI infrastructure (model serving, orchestration, autoscaling).
  • Understanding of data pipelines, data governance, security, APIs, and systems integration - Excellent communication you'll translate between technical teams and business stakeholders .
  • Track record of delivering AI systems into production
  • Preferred / Nice-to-Haves

  • Familiarity with AI governance, explainability, monitoring, and safety
  • Knowledge of reinforcement learning libraries (e.g. RLlib, Ray, OpenAI frameworks)
  • Experience with observability, continuous learning pipelines, adaptation in deployed agents
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