Consequential builds for practitioners who own their work.

About Common Works

Common Works is an AI-native services firm built for private equity. We work at the intersection of investment diligence and technical delivery, helping PE sponsors and their portfolio companies translate AI from a thesis into operating outcomes: higher EBITDA, better margin, and durable capability inside the business.

Our model isn’t consulting. We are builders who understand how deals work, and deal practitioners who can ship AI transformations. We forward deploy engineers, architects, and operators inside portfolio companies, work alongside management, and are accountable for the outcome.

The Role

Data & AI Solutions Architects are the senior technical authority on a Common Works engagement. You set the architecture (data flows, integration points, model selection, retrieval design, security model, monitoring approach) and the engineering team builds against it. Getting that design right means working from what actually exists: mapping client systems firsthand, assessing data quality directly, and making explicit calls on what is buildable under real constraints before the team commits to a direction.

This is a staff-level engineering role. You are not handing off a design and stepping back; you stay in the implementation, reviewing plans and engineering work, making tradeoff decisions when integration realities diverge from the original architecture, and ensuring what ships is maintainable after handoff. The ability to move fast inside messy environments (legacy infrastructure, fragmented data, limited access windows) is as important as the ability to design clean systems.

Key Responsibilities

• Map client systems and data landscapes (CRM, ERP, BI, data warehouses, document repositories, and operational databases) and build a firsthand picture of what exists, where data lives, and what is accessible
• Assess data readiness for priority AI use cases: availability, quality, historical depth, access permissions, API coverage, latency, and security constraints, and make the call on what is buildable now versus what requires foundational work first
• Define technical architectures for AI-enabled workflows: data flows, integration points, model usage patterns, retrieval design, human-in-the-loop controls, security model, and monitoring approach
• Lead the engineering team through implementation: set workstreams, review plans and implementation work, make tradeoff decisions when realities diverge from the design, and coordinate with client IT, data, and security teams
• Define MVP scope and what done looks like: which capabilities ship first, what gets deferred, and what reliability, observability, and access requirements the system must meet before handoff
• Identify technical risk (data quality issues, integration complexity, model output failure modes, security exposure) with enough specificity to drive engineering decisions

Qualifications

Required

• 5+ years as a senior or staff-level engineer with ownership of technical architecture across systems that integrate multiple data sources and enterprise platforms
• Track record as a technical lead: setting direction for a team, reviewing implementation work, and making hard design tradeoffs under delivery pressure
• Hands-on depth in enterprise data systems and integration across mid-market environments: ERP, CRM, BI, operational databases, data warehouses, and third-party APIs
• Practical experience with applied AI and LLM application patterns in production: RAG, semantic search, document extraction, agentic workflows, and structured output
• Strong data engineering foundation: pipeline reliability, data quality patterns, schema normalization, and the judgment to know when data is trustworthy enough to support a use case
• Experience working inside real operational constraints (legacy infrastructure, limited access, variable data quality) and shipping systems that hold up anyway

Preferred

• Background in client-facing technical delivery (services, consulting, or embedded engineering) where you owned architecture across multiple engagements
• Experience with modern data stacks (Snowflake, BigQuery, Databricks, dbt) alongside legacy system environments
• Exposure to agentic system design, evaluation frameworks for LLM systems, or AI security review in enterprise settings