AI Solutions Engineer

AI Solutions Engineers are the hands of the AI program. You embed beside a business team, understand their work deeply, and rewire it with AI — then hand the result over and measure the win. This is not a help desk and not a build queue: you're an elite generalist deployed to the highest-value friction in the company, owning problems end-to-end, from discovery sessions to the working solution to the story of what it saved.

Primary Job Duties

1.Discover & record. Embed with a team, shadow and record how the work actually gets done, and find the high-volume, high-friction, rules-heavy workflows worth rewiring.

2.Define the SOP before AI. Make the current process explicit — you can't improve what you haven't written down.

3.Engineer the AI-driven SOP — human-in-the-loop. Redesign the workflow around AI, keeping review and approval checkpoints wherever judgment, risk, or client trust require them. AI drafts; humans own.

4.Build the assets. Turn the redesign into reusable capability — prompts, playbooks, harness workflows, automations (n8n / ChatGPT / Claude / agentic tooling), and small bespoke apps or integrations. You'll be able to work with developers as complex scenarios arise.

5.Capture the win. Help employees log board-grade wins in the AI Wins Tracker, convert their hurdles into wins, and package repeatable patterns into the shared playbook / prompt library for other teams.

6.Clear the path. Partner with IT, security, and infrastructure on access, connectors, and permissions; escalate recurring blockers into infrastructure and policy asks.

7.Create the moments people remember. Notice the task someone dreads and build the thing that makes it disappear — the tailored touch that produces trust and success.

8.Business & Customer workflows — operations across TFS, Class Action, Restructuring, and customer-facing teams: document and case work, claims, proposals, correspondence, support.

9.Internal & Technical workflows — automation, reporting, data plumbing, and agentic development /QA enablement for the technical teams.

Ø Hands-on fluency with modern AI assistants — ChatGPT and Claude (Desktop / Code / agentic modes) — used daily to get real work done, not just demoed.

Ø Prompt engineering — you can design, test, and iterate prompts and reusable prompt libraries that hold up in production, and you understand context, tools, and where models fail.

Ø Workflow automation fluency (n8n / Zapier / similar) — you can build working automations end-to-end, including multi-step flows, triggers, and API calls.

Ø Working with APIs and integrations — you can read API docs, wire systems together with

Ø REST/webhooks/auth, and move data between tools without hand-holding.

Ø Light scripting / building — comfortable enough in C#, SQL & JavaScript (or a low-code equivalent) to build small apps, scripts, and glue, and to read code others wrote.

Ø Process mapping — you can sit with a team, extract how the work really happens, and write a clear SOP before you touch AI.

Ø Data literacy — spreadsheets, structured data, and basic SQL; you can quantify a before/after and defend the number.