Overview
AI Transformation Architect Jobs in Texas, United States at Cortiqa
Title: AI Transformation Architect
Company: Cortiqa
Location: Texas, United States
Company Description
We believe that a modern CoE is not just a delivery hub—it is the brain of the organization. By integrating predictive analytics, and autonomous agents into the very DNA of our services, we empower organizations to move from ‘managing tasks’ to ‘orchestrating outcomes.’ We don’t just set up teams; we build human-AI hybrid workforces that drive exponential value.
Role Description
This role is based in United States with preference to candidates from Texas / Austin. We are seeking a battle-tested AI Transformation Architect with 13-15 years of experience to lead the fundamental redesign of our operational DNA. This is not a research role; it is a high-velocity, value-creation engine. You will bridge the gap between cutting-edge AI capabilities and C-suite business objectives, driving EBITDA growth through intelligent automation and agentic systems.
Preferablly coming from a Private Equity or high-growth startup background, you understand that speed is a competitive advantage. You will act as a primary influencer to the Technology leaders & executives, navigating technical debt and legacy structures to implement a "Model Agnostic" AI ecosystem. You are equally comfortable architecting a multi-agent workflow as you are defending the ROI of an AI initiative to a board of directors.
Key Responsibilities
- Strategic AI Roadmap: Define and execute a multi-year AI vision focused on rapid value creation and scalable business impact.
- CTO Advisory & Influence: Partner with technical leadership to pivot roadmaps toward AI-first thinking, balancing long-term technical health with short-term commercial wins.
- Agentic Ecosystem Design: Architect enterprise-grade autonomous agent workflows (LangGraph, CrewAI) to replace or augment complex manual business processes.
- Infrastructure Modernization: Lead the selection and integration of the AI stack, including Vector Databases (Pinecone/Milvus), LLM Orchestration, and observability frameworks (LangSmith).
- Commercial De-risking: Establish governance and model-agnostic frameworks to ensure the organization remains flexible as the LLM landscape evolves.
Technical Stack
Orchestration & Agents : LangChain, LangGraph, CrewAI, AutoGPT, LlamaIndex.
Context & Memory : RAG Architectures, Vector DBs (Pinecone, Weaviate), Knowledge Graphs (Neo4j).
LLMOps & Observability : Weights & Biases, LangSmith, LiteLLM (for model routing/cost control).
Infrastructure : Cloud Native (AWS/Azure/GCP), Kubernetes, Serverless AI, Docker.
Required Profile
- Tenure: 13-15 years of progressive experience in Software Engineering, Data Science, and AI Leadership.
- Ecosystem Experience: Proven track record in Private Equity portfolio companies or high growth startups where "Time-to-Value" is the primary metric.
- Communication: Mastery of "Business English"—the ability to translate technology into business outcomes such as IRR, CAC reduction, and EBITDA impact.
- Execution: A "70% certain and moving" mindset. You prefer shipping working agents over producing perfect architectural diagrams.
- Education: MS/PhD in Computer Science, Data Science, or an MBA with a deep technical foundation.