AI / ML Engineer
Design and deploy applied AI systems (retrieval, agents, evals) that hold up in production.
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
AI/ML Engineers at Common Works build and evaluate the applied AI systems that power portfolio company products and workflows. The work is grounded in practical delivery: retrieval pipelines that return the right results, agents that complete tasks reliably, and evaluation frameworks that catch regressions before users do. You are not publishing research; you are shipping systems that operators depend on to do their jobs.
You own the AI layer of an engagement end-to-end, from data preparation through model integration and production monitoring, working directly with engineers and architects to ensure clean integration into the broader product.
Key Responsibilities
- Design, build, and evaluate LLM-powered systems: retrieval pipelines, agentic workflows, structured extraction, and classification
- Build evaluation frameworks that measure real system performance against business-relevant metrics, not just benchmark scores
- Own data preparation, chunking, embedding, and indexing pipelines for retrieval and context management
- Make pragmatic decisions on model selection, context architecture, and cost/quality tradeoffs
- Monitor AI systems in production: identify quality degradation, latency issues, and edge case failures before they reach users
- Work with build pod engineers to integrate AI capabilities cleanly into product surfaces and operator workflows
Qualifications
Required
- 5+ years of applied AI or ML engineering experience with a focus on production delivery, not research
- Hands-on experience building LLM-powered applications: RAG, structured output extraction, agents, or classifier systems
- Depth in agentic system design: tool use, multi-step reasoning, memory patterns, and failure recovery
- Strong Python proficiency and fluency with the core applied-AI stack: model APIs (OpenAI, Anthropic, Gemini), vector databases, and orchestration frameworks
- Experience designing and running evaluations for language model systems in production
- Solid understanding of the data quality and pipeline requirements that make AI systems reliable
Preferred
- Production observability experience: logging, tracing, and monitoring for AI system behavior
- Background in a multi-client services or consulting context where you owned AI systems across engagements
Join us.
Tell us who you are and what you’ve shipped.