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Job Description

Insight Global is seeking a Lead AI Architect to work a hybrid schedule onsite in Dallas or Houston, TX, with its fortune 500 Civil Engineering consultant client. Responsibilities will include the following:

AI Architectural and Strategy:

Define and maintain the overarching GenAI, LLMs, RAG pipelines, and autonomous agent systems

Design and implement multiple agent orchestration workflows and agentic frameworks.

Evaluate and select AI/Agentic tools, frameworks, and platforms (ex: LangChain, Semantic Kernel, Vertex AI, Azure OpenAI, AWS Bedrock, LangGraph, CrewAI)

Align architectural decisions with business performance metrics, latency, security, cost, and explainability requirements.

Assess emerging AI technologies and trends, recommending their adoption where appropriate

Design scalable architectures that support composability, modularity, observability, and scalability of AI solutions, ensuring alignment with business strategy, data strategy, and technology roadmap

Participate as a key stakeholder in the development of our AI Roadmap

Solution Design:

Collaborate with cross-functional teams, including data architects, data scientists, enterprise architecture, security, AI product managers, and business stakeholders

Guide proof-of-concept development and prototype evaluations to validate architecture decisions

Provide architecture review, feedback, and mentorship to AI engineering teams

Conduct structured build-vs-buy evaluations and contribute to platform roadmap decisions

Governance:

Embed responsible AI principles, privacy, and governance frameworks into system design

Ensure AI architecture complies with enterprise standards, security policies, and regulatory frameworks

Partner with AI governance, data governance, security, and compliance teams to implement transparency and auditability

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Skills and Requirements

BA/BS plus at least 10 years of relevant architecture experience or demonstrated equivalency of experience and/or education

At least 4 years of experience in ML or AI systems design and architecture

Proven expertise in designing and deploying enterprise-grade genAI, RAG models, agentic AI, and ML pipelines.

Working knowledge/experience with interoperability protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) for cross-platform agent communication.

Experience designing/implementing solutions with agentic frameworks (e.g., LangChain, Azure AI Agent Services, n8n, Autogen, etc.).

Working knowledge of AI Gateway implementation for enforcing guardrails, monitoring, and centralized model access control.

At least 3 years of experience building AI solutions in AWS or equivalent

Ability to build AI POCs and design for enterprise production

Leadership & Collaboration:

Excellent collaboration and communication skills are key

Must be comfortable engaging with key stakeholders and IT top-level leadership

Proven ability to lead and influence cross-functional teams, including software engineer teams and software product managers

Demonstrated ability to stay on top of AI trends and best practices Understanding of MLOps and DevOps practices including CI/CD for models, observability, and rollback GitHub, GitHub Actions

CI\CD pipelines using YML, AWS CloudFormation, Terraform

Is intellectually curious in order to keep on top of new concepts in AI/ML, trends, and best practices

Containerization tools like Kubernetes and Docker

Programming languages/frameworks including C#, Python, JavaScript, JSON

Azure DevOps for recording and tracking of Epics, Features and User Stories