Job Description

Role summary

You will be the in-house AI leader for commercial operations - translating business needs into practical, secure, measurable AI solutions. This is a hands-on builder role: you will prototype quickly, integrate with existing tools (CRM/ERP/email/web), and operationalize what works through change management, documentation, training, and governance.



What success looks like (first 90 days)

Map end-to-end commercial workflows (lead-to-order, order-to-cash touchpoints, customer support intake-to-resolution) and quantify top 10 time/cost frictions. Deliver 2-3 production pilots with measurable outcomes (e.g., faster quote turnaround, higher

lead-to-meeting conversion, reduced support backlog).

Establish an AI usage policy (data handling, approvals, prompt hygiene, human-in-the-loop), plus a simple ROI tracker and backlog process. Enable the team: training sessions, playbooks, and templates so AI becomes a repeatable capability -

not a one-off project.



Key responsibilities

Discovery & prioritization: run workshops with Sales/Marketing/CS/Product to identify high-value AI opportunities; build a roadmap with effort vs impact. Solution design & delivery: prototype and deploy AI solutions (LLM assistants, RAG search,

automation agents, analytics models) with clear acceptance criteria and KPIs.

Customer service automation: implement AI-assisted triage, knowledge base search, ticket summarization, suggested replies, and escalation logic. Marketing & lead generation: build AI workflows for segmentation, outreach personalization, content

drafts, account research, and lead scoring support.

Sales enablement: create AI tools for call/meeting prep, note-to-CRM capture, proposal/quote drafting, objection handling libraries, and competitive intel briefs. Product & voice-of-customer: use AI to mine customer feedback, map themes, and translate insights

into product requirements and roadmap inputs.



Integration: connect AI workflows with CRM/ERP/helpdesk/email (e.g., Salesforce/Dynamics/HubSpot, SAP/NetSuite, Zendesk/Freshdesk) using APIs or automation tools. Governance & risk: ensure compliance with data privacy, IP, and security requirements; implement

human review steps and auditability.

Change management: train users, document workflows, create reusable prompt libraries, and continuously improve based on feedback and metrics.

Required qualifications

5+ years in a role bridging business and technology (commercial ops, RevOps, sales enablement, product ops, digital transformation, or similar). Hands-on experience delivering AI/automation solutions into real workflows (not just research or

dashboards).

Comfortable with modern LLM tools and concepts: prompt engineering, function calling/tools, retrieval-augmented generation (RAG), evaluation, and guardrails. Strong process mindset: can map workflows, define requirements, and measure outcomes (cycle time,

conversion, CSAT, cost-to-serve).

Working knowledge of data handling: CSV/SQL basics, data quality, and how to connect systems via APIs/webhooks. Excellent communication: can translate technical choices into business value and train non-technical

teams.



Preferred qualifications

Bachelor's in Engineering, Computer Science, Data Science, or equivalent experience (MBA/Commercial background welcomed when paired with hands-on delivery). Experience with one or more: CRM (Salesforce/Dynamics/HubSpot), ERP (SAP/NetSuite), helpdesk

(Zendesk/Freshdesk), marketing automation, or CPQ tools.

Python proficiency for scripting and lightweight model work; SQL for analysis; familiarity with JavaScript is a plus. Experience with cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI) and basic DevOps

practices (Git, environments, deployment).

Familiarity with data privacy and security concepts (PII handling, access control, logging, vendor risk review).

Typical technical toolkit

LLM platforms: OpenAI/ChatGPT Enterprise or Azure OpenAI (preferred for enterprise controls), Anthropic/Google (where available). Automation: Power Automate, Zapier, Make, n8n; email and calendar integrations; webhooks. Data: Excel/Sheets, SQL (basic), BI tools (Power BI/Tableau), Python (pandas). Knowledge/RAG: document ingestion, embeddings, vector databases (Pinecone, Weaviate, FAISS) or built-in enterprise search connectors. Apps: CRM/ERP/helpdesk APIs, internal portals, and lightweight web apps for assistants.

Benefits

Medical Dental Vision Life Insurance * 401K