AI & Automation Engineer

About Emergence

Emergence is a PE holdco backed by the Pritzker Organization, specializing in acquiring and scaling high-potential B2B SaaS businesses. We combine operational rigor with a growth equity mindset to drive ARR growth and profitability across our entire portfolio of companies. Our mission is to build intelligent infrastructure that compounds value across multiple portfolio businesses.

The Role

As an AI & Automation Engineer at Emergence, you'll build the intelligence layer that runs across our entire SaaS portfolio. This isn't internal tooling—you're architecting agentic systems, LLM pipelines, and production integrations that become the operational backbone of multiple B2B SaaS businesses. Your work will directly impact how we acquire, integrate, and scale companies.

Key Responsibilities

• Design and Ship Agentic Systems: Build multi-step LLM workflows using Claude, OpenAI, or equivalent technologies. Implement tool use, memory management, structured output extraction, and sophisticated failure handling for production environments.
• MCP Integration Architecture: Build and maintain Model Context Protocol integrations connecting internal tools, portfolio company systems, and external data sources into reliable, observable pipelines with comprehensive monitoring.
• RAG Pipeline Ownership: Own end-to-end RAG architecture including chunking strategy, embedding selection, retrieval evaluation, and continuous quality improvement against real-world usage data and user feedback.
• Production Python Development: Write production-grade Python for data pipelines, integration scripts, and scheduled jobs. Ensure code is modular, well-logged, and runs reliably via BullMQ-backed queues on the Node/TypeScript stack.
• REST API Integration Mastery: Build and maintain integrations across Salesforce, Ashby, SeekOut, Slack, and Google Workspace. Own reliability, implement sophisticated retry logic, and deploy comprehensive failure alerting.
• Browser Automation Layer: Own and evolve the browser automation infrastructure using Playwright-based scrapers with persistent session management and anti-detection handling. Evaluate and integrate managed alternatives as the ecosystem matures.
• LLM Evaluation Frameworks: Implement systematic eval frameworks to test LLM outputs, catch regressions before production deployment, and continuously improve model performance.
• AI Cost Optimization: Track, analyze, and optimize AI costs across models and workflows through intelligent model routing, prompt caching strategies, and token efficiency improvements.

What We're Looking For

Must-Have Experience:

• Production-shipped agentic systems with multi-step LLM workflows, tool use, and real complexity—not just single prompt calls or chatbot wrappers.
• End-to-end RAG pipeline experience: chunking strategy, embedding model selection, and retrieval quality evaluation against production metrics.
• Strong Python proficiency including async patterns, modular code architecture, structured logging, and production-grade reliability practices.
• Playwright or Selenium expertise managing real headless browsers in production environments with proper session handling and scaling.
• Deep REST API integration experience covering rate limiting, webhook handling, retry logic, OAuth flows, and API state management.
• Experience integrating LLMs with live tools and data sources via APIs, function calling, or protocol layers. MCP experience is strongly preferred.
• GitHub Actions proficiency for CI/CD, automated testing, and deployment workflows.
• Comfortable working across Python and Node/TypeScript ecosystems without requiring hand-holding.
• Ability to debug complex distributed systems and own problems end-to-end from conception through production monitoring.

Nice-to-Have Skills

• Experience with vector databases (Pinecone, Weaviate, Milvus) and embedding workflows at scale.
• Familiarity with LLM observability tools and cost monitoring platforms.
• Understanding of prompt engineering principles and LLM-specific optimization techniques.
• Experience with headless browser management at scale and anti-bot circumvention techniques.
• Knowledge of concurrent request handling and queue-based job processing systems.

Why Join Emergence

You'll work on AI infrastructure that actually matters—systems that power multiple B2B SaaS businesses serving thousands of end users. Rather than experimenting in isolation, you'll build production systems with real constraints, real users, and real business impact. You'll have autonomy to architect solutions, own problems end-to-end, and see your work compound across our entire portfolio.

This role offers the unique combination of startup-style impact with the resources and scale of a PE-backed organization. You'll work alongside experienced operators and entrepreneurs while solving genuinely complex technical problems at the intersection of AI, automation, and systems integration.

💰 Compensation not publicly listed. Market estimate for similar roles: from $80K, varying by experience and location.