AI Engineering Architect

AI Engineering Architect & Technical Coach The AI Engineering Architect & Technical Coach is a hands‑on engineering leader responsible for designing, building, and guiding the technical foundation of an enterprise‑scale AI transformation program. This role bridges architecture, execution, and mentorship, ensuring that AI experiments, data pipelines, and production systems are technically sound, scalable, and reusable across squads. You’ll work side by side with engineers in the AI Skunk Works, Data Foundations, and Engineering Excellence squads setting engineering standards, unblocking delivery, and embedding best practices in cloud, data, and AI systems. Your north star: make sure every AI experiment can scale cleanly, securely, and reliably. Design the technical architecture for AI and data initiatives — including ingestion, transformation, and model deployment pipelines. Define and document reference architectures, API standards, and reusability frameworks. Collaborate with data engineers to build scalable ETL/ELT pipelines and feature stores that feed AI models. Evaluate and optimize cloud infrastructure (AWS, Azure, or GCP) for cost, performance, and resilience. Architecture blueprints, reference implementations, technical documentation. Hands‑On Development & Coaching Build or review Python scripts, SQL queries, APIs, and pipeline automation. Coach engineers on coding standards, CI/CD automation, observability, and testing practices. Conduct stability reviews and code walk‑throughs to raise engineering quality. Lead “Engineering Excellence” workshops on reliability, scalability, and AI deployment hygiene. Working prototypes, CI/CD templates, best‑practice repositories, coaching sessions. Partner with Skunk Works leads to make AI experiments technically viable. Set up data pipelines, connectors, and lightweight back‑end APIs for pilot experiments. Ensure each experiment’s architecture supports clean handoff to production once validated. Evaluate and integrate AI tools, APIs, or SDKs (e.g., OpenAI, Hugging Face, Vertex AI, Azure AI Studio). Engineering Quality & Platform Improvement Define and enforce engineering excellence standards: stability, scalability, and security. Implement automation in build, deploy, and monitoring pipelines. Collaborate with the Engineering Excellence squad to uplift delivery velocity and reduce incidents. Automated deployment pipelines, quality dashboards, remediation plans. Work closely with the Director of AI Practice & Transformation on cross‑squad technical strategy. Collaborate with the AI & Data Strategy Lead to ensure architecture aligns with data availability and governance rules. Build deep trust with both the client’s technical teams and Intellibus engineers. Technical reviews, architecture alignment sessions, mentoring reports.

Experience:
8–15 years in software, data, or AI engineering; Proficiency in Python, SQL, and cloud‑native architectures (AWS, Azure, or GCP). Hands‑on experience with data‑pipeline frameworks (Airflow, dbt, Kafka, Spark). Familiarity with ML model deployment (MLflow, SageMaker, Vertex AI, or custom API deployment). Bonus: Experience in retail systems (POS, inventory, merchandising, supply chain) or large‑scale data integrations. Deliver reusable AI Experimentation Framework for Skunk Works pilots (scripts, templates, pipelines). Establish CI/CD and data ingestion pipelines supporting initial experiments. Conduct first Engineering Excellence Bootcamp for existing engineers. Lead stability and code‑quality review across squads. 50% Architect → designs reusable systems and processes for AI Experimentation. ~20% Coach → teaches and unblocks other coaches in the Engineering Excellence Squad. ~ Schedule a 15‑minute Video Call with someone from our Team. ~ 1 Proctored GQ (Q&A) & Slideware (Google Slide Presentation) Assessment. ~ Contract Design, Art/Creative, and Information Technology IT Services and IT Consulting