AI Lead Architect

Infinitive Inc•Ashburn, VA

3d•$140,000 - $210,000

About The Position

Infinitive is seeking a visionary, hands-on AI Architect to lead the architecture, rollout, and operational governance of its AI Factory. The role bridges modern cloud data architectures with generative and agentic AI tooling, and defines how Infinitive and its clients use AI-assisted engineering to accelerate software delivery, data platform modernization, and code migration. The position involves designing and operating an enterprise AI Factory for legacy-to-modern code conversions and automated data engineering; configuring agentic developer workflows and guardrails; creating reusable conversion templates; and building automated validation and reconciliation frameworks. It also includes integrating quality checks into CI/CD pipelines, writing production-grade Python, PySpark, and SQL, and designing and optimizing Databricks lakehouse solutions. The AI Lead Architect establishes governance for AI-assisted engineering, including risk controls and quality gates for code synthesis, data security, intellectual property, and prompt use. The role measures engineering productivity and delivery outcomes, and provides technical leadership and coaching to technical leads, data engineers, and consulting delivery teams. The role is based in the U.S.; the posting does not specify a workplace arrangement.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, or a related technical discipline.
At least 7 years of progressive experience in data architecture, software engineering, or technical delivery.
At least 2 years of experience architecting or scaling modern AI/LLM-assisted engineering workflows.
Expert proficiency in Python, PySpark, and SQL.
Deep hands-on experience structuring repository instructions, system prompts, guardrails, and context windows for developer tools such as Claude Code, GitHub Copilot, or similar agentic coding environments.
Proven track record designing scalable Databricks architectures alongside AWS or Azure environments.
Strong background designing CI/CD pipelines, such as GitHub Actions, GitLab CI, or Azure DevOps, with automated testing, linting, and data reconciliation suites.
Exceptional communication skills and the ability to explain AI governance frameworks, architectural patterns, and productivity impact metrics to executive stakeholders and technical teams.

Nice To Haves

Databricks certification; Databricks Certified Data Engineer Professional, Solutions Architect, or Machine Learning Professional certifications are strongly preferred.
Experience architecting migration pipelines that convert legacy data platforms such as Netezza, Teradata, Oracle, Informatica, or SSIS to Databricks Lakehouse.
Working knowledge of FinOps principles and token or compute cost attribution for large-scale AI workloads.

Responsibilities

Design, build, operate, and continuously refine Infinitive’s enterprise AI Factory for code conversions and automated data engineering workflows.
Create system prompts, project instructions, rule sets, and operational guardrails for Claude Code and other AI coding agents.
Develop and maintain modular conversion templates for technical archetypes, including legacy ETL or SQL to PySpark and on-premises warehouse models to lakehouse architectures.
Design automated validation frameworks and reconciliation test harnesses to verify data integrity, schema consistency, and business logic parity between legacy implementations and AI-generated code.
Integrate static analysis, linters, and architectural pattern checks into enterprise CI/CD workflows to detect regressions, drift, and anti-patterns before deployment.
Write production-grade Python, PySpark, and advanced SQL for data pipelines, lakehouse architectures, and distributed computing tasks.
Design and tune Databricks lakehouse solutions with attention to performance, security, and cost efficiency.
Establish policies, risk controls, and quality gates for AI-assisted code synthesis, data security, intellectual property safety, and prompt governance.
Define, track, and report metrics for engineering acceleration, developer velocity, cycle-time reduction, defect rates, and return on investment.
Coach technical leads, data engineers, and consulting delivery teams adopting AI-assisted development tools.
Communicate AI governance frameworks, architectural patterns, and productivity impact metrics to executive stakeholders and technical teams.