AI Automation Engineer

Job description

Beghou brings over three decades of experience helping life sciences companies optimize their commercialization through strategic insight, advanced analytics, and technology. From developing go-to-market strategies and building foundational data analytics infrastructures to leveraging artificial intelligence to improve customer insights and engagement, Beghou helps life sciences companies maximize performance across their portfolios. Beghou also deploys proprietary and third-party technology solutions to help companies forecast performance, design territories, manage customer data, organize, and report on medical and commercial data, and more. Headquartered in Evanston, Illinois, we have 10 global offices. *Our mission is to bring together analytical minds and innovative technology to help life sciences companies navigate the complexity of health care and improve patient outcomes.* About the role Beghou Consulting is building an AI-native operating model for life sciences consulting. We have a growing portfolio of AI initiatives — internal workflow transformation, client-facing products like Beghou Arc, governance frameworks, and go-to-market programs — led by a small, fast-moving team. This role is the builder on the team. You take a mapped consulting workflow — one that has been documented with its steps, handoffs, tools, and pain points — and turn it into an AI-enabled, partially or fully automated version. Your work is measured in working software: agents that run, automations that save hours, tools that consultants actually adopt. You will pair closely with other AI automation engineers and the Director, AI Innovation. You focus on the build. ### What you will do - Build AI-enabled versions of consulting workflows using tools like Claude Code, Codex, Cursor, VS Code, n8n, Python, and modern agent frameworks (RAG, MCP servers, orchestration layers) - Prototype quickly — ship a first working version in days, then iterate tightly with the consulting team that will use it - Instrument automations so we can measure usage and quantified time saved - Own the technical end of 1–2 delivery accelerators each year, from first build through internal deployment - Integrate with Beghou systems (SharePoint, GitHub, Azure, approved LLM endpoints) under the governance rules set by the AI team - Document what you build — architecture, prompts, known limitations — so others can extend it - Support end users during early rollout (bug triage, usability fixes, minor feature additions) ### What makes someone great at this - You are an AI-native builder. You reach for agents, LLMs, and automation tools the way most engineers reach for libraries. You have built things on your own time because you find it interesting, not because it was an assignment. - You ship. You go from idea to working prototype in days, not weeks. Your first version is rough and honest; your second version is useful. - You are comfortable with ambiguous problem statements. You talk to the person who will use the tool, understand what they actually need, and make build choices without waiting for a spec. - You have real engineering fundamentals. You can read and write Python or TypeScript, understand APIs, version control, and basic deployment. - You are a fast learner on the business side. You do not need to be a pharma consultant, but you want to understand the workflow you are automating so you can make good design trade-offs. ### Background that fits well - 2–5 years of combined experience across software engineering and AI/automation tinkering. Strong candidates often have a full-stack engineering background (Python, JavaScript, or similar) plus hands-on personal or professional AI projects. - Demonstrable portfolio of AI projects — agents, RAG systems, automations, MCP servers, LLM-powered tools — whether professional or personal. - Familiarity with at least one LLM at the API level (Claude, GPT, Gemini) and at least one automation tool (n8n, Zapier, Make, or custom Python). - MBA or equivalent business training is a plus but not required — build experience is valued over credentials. Nice to have - Experience with vector databases, retrieval systems, or agent frameworks - Prior exposure to consulting or analytics work environments - Contributions to open-source AI tooling, or a public project portfolio