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 ResponsibilitiesAI 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 (FinOps) 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 & RequirementsEducation & 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 architecting or scaling modern AI/LLM-assisted engineering workflows.
- Hands-on Languages & Frameworks: Expert proficiency in Python, PySpark, and SQL.
- AI Tooling & Prompt Architecture: Deep hands-on experience structuring repository instructions, system prompts, guardrails, and context windows for developer tools like Claude Code, GitHub Copilot, or similar agentic coding environments.
- Lakehouse & Cloud: Proven track record designing scalable architectures on Databricks alongside AWS or Azure environments.
- DevOps & Quality Automation: Strong background designing CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Azure DevOps) with automated testing, linting, and data reconciliation suites.
- Consulting & Leadership: Exceptional communication skills with the ability to articulate AI governance frameworks, architectural patterns, and productivity impact metrics to executive stakeholders and technical teams alike.
Preferred Qualifications- Experience architecting migration pipelines converting legacy data platforms (e.g., Netezza, Teradata, Oracle, Informatica, SSIS) to Databricks Lakehouse.
- Working knowledge of FinOps principles and token/compute cost attribution for large-scale AI workloads.
Infinitive is required by law in some jurisdictions to include a reasonable estimate of the compensation range for this role. The determination of this range includes various factors not limited to skill set, level, experience, relevant training, and licensure and certifications. Compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range for this role in the U.S. is $140,000 - $210,000.
Infinitive is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic protected by applicable federal, state, or local law.
