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.

• 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