Job Title: Agentic AI Solution Architect
 Location: Dallas, TX (Day 1 Onsite)
 Interview: In-person

Role Summary:
We are seeking an Agentic AI Solution Architect to design, architect, and lead the deployment of autonomous multi-agent AI systems across mission-critical operations, customer-facing platforms, and enterprise IT ecosystems. This role bridges business priorities, large-scale infrastructure, and next-generation AI capabilities to deliver high-value, safe, and scalable solutions. You will work closely with stakeholders across operations, IT, and customer experience to turn business challenges into working agentic AI designs ready for production.

Key Responsibilities:
- Architect end-to-end agentic AI ecosystems with orchestration, planning, and autonomous execution.
- Design multi-agent workflows for disruption management, crew scheduling, predictive maintenance, and customer rebooking.
- Integrate LLM agents with enterprise and systems (APIs, databases, IoT, crew management platforms).
- Define interfaces, APIs, data flows, governance, and security models for safe agent-to-system interactions.
- Establish architecture patterns, best practices, and design principles for scalable agentic AI deployments.
- Implement governance and safety guardrails, including escalation frameworks and human-in-the-loop oversight.
- Lead technical reviews, prototyping, validation, and iteration cycles to refine AI solutions.
- Mentor engineering and AI teams on agentic design, RAG patterns, and scalable AI deployment.
- Track emerging research in multi-agent systems, reinforcement learning, and self-adaptive AI, and apply relevant innovations.

 Required Skills
- 10+ years in IT/AI Solution Architecture, with 2–3 years in LLM/Agentic AI system design.
- Hands-on experience with LangChain, AutoGen, CrewAI, Azure AI Agent Framework.
- Strong expertise in RAG (Retrieval-Augmented Generation), embeddings, and vector DBs (Pinecone, Weaviate, FAISS).
- Proven track record in architecting distributed systems, microservices, and event-driven/streaming platforms.
- Cloud expertise: AWS, Azure, or GCP AI/ML platforms (model serving, orchestration, autoscaling).
- Strong integration background in APIs, event-driven architectures, and enterprise system interoperability.
- Experience delivering AI systems into production with attention to governance, observability, and continuous learning.
- Excellent communication skills for bridging technical teams and business stakeholders.

 Preferred / Nice-to-Haves
- Familiarity with AI governance, monitoring, explainability, and safety frameworks.
 - Experience with reinforcement learning libraries (Ray, RLlib, OpenAI frameworks).
 - Exposure to digital twin simulations for testing and validating agent behaviors