AI Automation Engineer

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