AI Architect

VSG Business Solutions LLC; Fort Worth (TX); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Airlines is seeking a seasoned Agentic AI Architect to define and drive the technical architecture for its Agentic System Layer (ASL) platform. This role requires deep expertise in software architecture, distributed systems, and production-grade agentic AI applications.

The ideal candidate will lead architectural decisions, mentor engineers, and collaborate with ML and software teams to deliver scalable, secure, and observable AI capabilities.

Qualifications

• 10+ years of software engineering and architecture experience.
• At least 3 years designing AI/ML platform systems.
• Hands-on experience with LLM orchestration frameworks such as LangGraph, LangChain, Semantic Kernel.
• Strong expertise in distributed systems, microservices, and event-driven design patterns.
• Strong API design experience (REST and gRPC).
• Strong proficiency in Python and experience with Java, Go, or TypeScript.
• Proven experience deploying production-grade agentic AI systems at scale.
• Hands-on experience with prompt engineering, tool-use patterns, RAG pipelines, and LLM integration.
• Experience implementing agent reliability, observability, monitoring, and safeguards.
• Strong architectural decision-making and leadership skills.

Responsabilités

• Design and evolve architectures for multi-agent orchestration systems, tool-use frameworks, and LLM integration pipelines.
• Establish architectural patterns for agent reliability, observability, monitoring, security guardrails, and fault tolerance.
• Lead technical design reviews and document architectural decisions through ADRs.
• Collaborate with ML engineers and software engineers to deliver scalable, secure, and maintainable platform components.
• Evaluate and integrate agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, and AutoGen.
• Define API contracts, data flow patterns, and integration standards across the AI platform ecosystem.
• Design and implement LLM-powered applications using prompt engineering, tool calling, and RAG.
• Mentor engineers on architecture best practices and production-grade AI system development.
• Evaluate architectural trade-offs to ensure high availability and graceful degradation at enterprise scale.

Connaissances

Software architecture

Distributed systems

API design

Python

Engineering leadership

LLM orchestration

Prompt engineering

Production-grade AI systems

Outils

LangGraph

LangChain

Semantic Kernel

CrewAI

AutoGen

Kubernetes

AWS

Azure

GCP

Pinecone

Weaviate

pgvector

Description du poste

Job Summary

Airlines is seeking a seasoned Agentic AI Architect to define and drive the technical architecture for its Agentic System Layer (ASL) platform. This role requires deep expertise in software architecture, distributed systems, and production-grade agentic AI applications. The ideal candidate will combine strong architectural leadership with hands-on engineering experience to design scalable, secure, reliable, and observable AI systems for enterprise environments.

Key Responsibilities

• Design and evolve architectures for multi-agent orchestration systems, tool-use frameworks, and LLM integration pipelines.

• Establish architectural patterns for agent reliability, observability, monitoring, security guardrails, and fault tolerance.

• Lead technical design reviews and document architectural decisions through Architecture Decision Records (ADRs).

• Collaborate with ML engineers and software engineers to deliver scalable, secure, and maintainable platform components.

• Evaluate and integrate agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, and AutoGen.

• Define API contracts, data flow patterns, and integration standards across the AI platform ecosystem.

• Design and implement LLM-powered applications using prompt engineering, tool calling, and Retrieval-Augmented Generation (RAG).

• Mentor engineers on architecture best practices and production-grade AI system development.

• Evaluate architectural trade-offs to ensure high availability, graceful degradation, and reliable operation at enterprise scale.

Required Qualifications - Must Have

• 10+ years of software engineering and architecture experience.

• At least 3 years of experience designing AI/ML platform systems.

• Hands-on experience with LLM orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, or similar technologies.

• Strong expertise in distributed systems, microservices architecture, and event-driven design patterns.

• Strong API design experience, including REST and gRPC.

• Strong proficiency in Python and experience with at least one of Java, Go, or TypeScript.

• Proven experience building and deploying production-grade agentic AI systems or LLM-powered applications at scale-not just prototypes or proof-of-concepts.

• Hands-on experience with prompt engineering, tool-use patterns, RAG pipelines, and LLM integration.

• Experience implementing agent reliability, observability, monitoring, and production safeguards.

• Strong architectural decision-making, technical communication, and engineering leadership skills.

Preferred Qualifications - Nice to Have

• Experience with Kubernetes, container orchestration, and cloud platforms such as AWS, Azure, or GCP.

• Familiarity with MLOps/LLMOps tooling and practices.

• Experience with vector databases such as Pinecone, Weaviate, or pgvector.

• Knowledge of knowledge graphs, multi-agent design patterns, and agent evaluation or benchmarking frameworks.

• Airline or travel industry experience.

• TOGAF or a similar architecture certification.

Ideal Candidate Profile

The ideal candidate has successfully designed, built, and deployed production-grade agentic AI systems. They can explain and defend multi-agent architecture decisions, evaluate LLM orchestration strategies, and demonstrate hands-on coding ability. They understand the importance of reliability, scalability, observability, security, and graceful degradation in enterprise AI platforms. They are comfortable challenging technical decisions constructively and mentoring engineering teams.