AI Automation Engineer
2+ years building AI/automation projects; strong engineering fundamentals (Python or TypeScript), APIs, version control; experience with LLM APIs and automation tools; portfolio of agent/RAG/automation work.
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.
About the role
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 ClaudeCode, 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.Youreach foragents, LLMs, and automation tools the way most engineersreach forlibraries. You have built thingsonyour 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 theyactually 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 makegood designtrade-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 notrequired— build experience is valued over credentials.
Nice to have
Experience with vector databases, retrieval systems, or agent frameworks
Prior exposure to consulting or analyticsworkenvironments
Contributions to open-source AI tooling, or a public project portfolio