AI Lead Architect

About Infinitive:

Infinitive is a data and AI consultancy that enables its clients to modernize, monetize and operationalize their data to create lasting and substantial value. We possess deep industry and technology expertise to drive and sustain adoption of new capabilities. We match our people and personalities to our clients' culture while bringing the right mix of talent and skills to enable high return on investment.

About

Infinitive:

Infinitive is a data and AI consultancy that enables its clients to modernize, monetize and operationalize their data to create lasting and substantial value. We possess deep industry and technology expertise to drive and sustain adoption of new capabilities. We match our people and personalities to our clients' culture while bringing the right mix of talent and skills to enable high return on investment.

Infinitive has been named "Best Small Firms to Work For" by Consulting Magazine 9 times most recently in 2026. Infinitive has also been named a Washington Post "Top Workplace", Washington Business Journal "Best Places to Work", and Virginia Business "Best Places to Work".

Position Overview

Infinitive is seeking a visionary and hands-on AI Architect to lead the architecture, rollout, and operational governance of our AI Factory. In this pivotal role, you will bridge modern cloud data architectures and generative/agentic AI tooling. You will be responsible for defining how Infinitive and its clients accelerate software delivery, data platform modernization, and code migration using AI-assisted engineering. From configuring agentic workflows with Claude Code to establishing automated reconciliation test benches and measuring developer throughput, you will set the engineering standards for the next era of delivery.

Key Responsibilities AI Factory Architecture & Tooling

• Architect and Operate the AI Factory:
  Design, build, and continuously refine Infinitive's enterprise AI Factory ecosystem, enabling rapid legacy-to-modern code conversions and automated data engineering workflows
• Claude Code & Agentic Workflows:
  Formulate system prompts, structured project instructions, rule sets, and operational guardrails for developer tooling, including Claude Code and related AI coding agents
• Conversion Archetypes & Blueprints:
  Develop, standardize, and maintain modular conversion templates categorized by technical archetype (e.g., legacy ETL/SQL to modern PySpark, on-prem warehouse models to lakehouse architectures)
• Automated Test & Reconciliation Harness:
  Architect robust, automated validation frameworks and reconciliation test harnesses to verify data integrity, schema consistency, and business logic parity between legacy source implementations and AI-generated code
• CI/CD & Automated Pattern Enforcement:
  Integrate automated static analysis, linters, and architectural pattern-checking pipelines directly into enterprise CI/CD workflows to catch regressions, drift, and anti-patterns prior to deployment

Technical Execution & Databricks Architecture

• Hands-on Engineering:
  Write clean, production-grade Python, PySpark, and advanced SQL for complex data pipelines, lakehouse architectures, and distributed computing tasks
• Platform Optimization:
  Design and tune modern lakehouse solutions on Databricks, ensuring adherence to performance, security, and cost-efficiency (Fin Ops) best practices

Governance, Quality & Productivity Measurement

• AI-Assisted Engineering Governance:
  Establish policies, risk controls, and quality gates governing code synthesis, data security, intellectual property safety, and prompt governance
• Productivity & Delivery Metrics:
  Define, track, and report quantitative metrics assessing engineering acceleration, developer velocity, cycle time reductions, defect rates, and ROI across AI Factory initiatives
• Mentorship & Technical Leadership:
  Serve as a subject matter expert and coach to technical leads, data engineers, and consulting delivery teams adopting AI-assisted development tools

Qualifications & Requirements Education & Certifications

• Education:
  
  Bachelor's degree in Computer Science, Information Systems, Software Engineering, or a related technical discipline
• Certification:
  Databricks Certified (Databricks Certified Data Engineer Professional, Solutions Architect, or Machine Learning Professional strongly preferred)

Professional Experience & Technical Skills

• Architecture
  
  Experience:
  
  7+ years of progressive experience in data architecture, software engineering, or technical delivery, with at least 2+ years…