AI Architect

Robotic Technologies Automation; Plano (TX); On-site; USD 210,000 - 270,000; Full time

Job summary

Robotic Technologies Automation is seeking an experienced AI Architect to lead the design and implementation of enterprise-scale AI solutions across the organization. The role requires deep expertise in Generative AI, LLMs, and Agentic AI, with strong hands-on architecture leadership to deliver production-grade AI systems.

You will collaborate with business stakeholders, data scientists, and engineering teams to translate requirements into robust AI-driven applications, define AI strategy, and

Qualifications

• 15+ years of technology experience in enterprise AI.

Responsibilities

• Define and lead enterprise AI architecture strategies, roadmaps, and standards.
• Design scalable, secure AI/ML and Generative AI solutions for enterprise applications.
• Lead the architecture and implementation of LLM-based applications (RAG, embeddings, vector DBs, semantic search).
• Architect Agentic AI solutions with autonomous agents, tool calling, orchestration, planning, and workflow automation.
• Evaluate foundation models, LLM platforms, AI frameworks, and technologies based on business needs.
• Collaborate with data engineering, ML, DevOps, app development, cybersecurity, and enterprise architecture teams.
• Lead architecture reviews, technical design discussions, PoCs, and technology evaluations.
• Provide technical leadership and mentorship to AI engineers, ML engineers, data scientists, and development teams.
• Drive transition of prototypes into production-grade enterprise solutions.

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-4-EAD,Other

Job Description

We are seeking an experienced AI Architect with 15+ years of overall technology experience to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence solutions. The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), Agentic AI, Machine Learning, cloud-native architectures, and enterprise application integration.

The AI Architect will define the organization's AI strategy, establish scalable and secure AI architectures, and lead the development of intelligent applications that solve complex business problems. This individual will work closely with business stakeholders, data scientists, engineering teams, and technology leadership to translate business requirements into robust AI-driven solutions.

The ideal candidate must possess strong hands-on technical expertise, architecture leadership capabilities, and experience delivering production-grade AI solutions across enterprise environments.

Define and lead enterprise AI architecture strategies, roadmaps, and implementation standards aligned with business objectives.

Design and architect scalable, secure, and high-performance AI/ML and Generative AI solutions for enterprise applications.

Lead the architecture and implementation of LLM-based applications, including Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, vector databases, and semantic search.

Design and implement Agentic AI solutions, including autonomous agents, multi-agent systems, tool calling, orchestration, planning, and workflow automation.

Evaluate and select appropriate foundation models, LLM platforms, AI frameworks, and technologies based on business and technical requirements.

Architect AI-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, and other relevant technologies.

Define enterprise integration patterns connecting AI solutions with APIs, microservices, databases, enterprise applications, and external systems.

Design cloud-based AI architectures using AWS, Microsoft Azure, or Google Cloud Platform.

Establish AI governance, responsible AI, security, privacy, compliance, and model risk management practices.

Design solutions addressing LLM security risks, including prompt injection, data leakage, model misuse, hallucinations, and unauthorized access.

Establish MLOps and LLMOps practices covering model deployment, monitoring, evaluation, versioning, performance optimization, and continuous improvement.

Define architecture standards for model observability, tracing, logging, latency management, scalability, and cost optimization.

Collaborate with data engineering, machine learning, DevOps, application development, cybersecurity, and enterprise architecture teams.

Lead architecture reviews, technical design discussions, proof-of-concepts, and technology evaluations.

Provide technical leadership and mentorship to AI engineers, ML engineers, data scientists, and development teams.

Drive the transition of AI/ML prototypes and proof-of-concepts into reliable, production-ready enterprise solutions.

Stay current with emerging AI technologies and recommend innovative approaches that deliver measurable business value.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.