Technical Sales Representative

Overview

You're joining a 2-person AI consulting firm as their first sales hire. You'll be prospecting, demoing, and closing consulting engagements focused on AI implementation and infrastructure. The founder mentions needing someone who can "listen to a long AI infrastructure conversation" then do the human work of building trust and closing deals. You're building the sales motion from scratch with minimal support.

Role Snapshot

Company Context

Stage: Pre-seed / Bootstrapped

Size: 2 employees (you'd be #3)

Growth: Just starting to formalize sales, likely founder-led sales until now

Market Position: Competing against hundreds of AI consultancies and system integrators in a very crowded market

GTM Reality

Pipeline Sources:

• 0% Inbound - no website content, no brand presence, no marketing engine
• 100% Outbound - you're building the list, running sequences, booking meetings
• Network/Referrals - likely the founder's network, but that's finite

SDR/AE Structure: You are both. No SDR support, no handoffs.

SE Support: You are the SE. You need to handle technical discovery and scoping yourself.

Competitive Landscape

Main Competitors: Accenture, Deloitte, McKinsey (enterprise), plus hundreds of boutique AI consultancies and freelancers (mid-market)

How They Differentiate: Unknown - likely founder's technical depth and ability to go deep on AI infrastructure

Common Objections:

• "Why not use our existing consultancy?"
• "How are you different from [other AI consulting firm]?"
• "Can you show us case studies?" (You probably don't have many)
• "What if we just hire in-house?"

Win Themes: Technical credibility, speed/agility vs big consultancies, founder's personal expertise

What You'll Actually Do

Time Breakdown

Prospecting (50%) | Active Deals (30%) | Internal/Admin (20%)

Key Activities

• Cold Outreach: Building prospect lists of companies implementing AI, crafting outbound sequences, making calls. You're responsible for generating 100% of your pipeline - no leads are coming to you.
• Technical Discovery: Getting on calls with engineering/data science teams, asking questions about their AI infrastructure stack, understanding their pain points around model deployment, scaling, tooling.
• Scoping & Proposing: Translating technical needs into project scopes, working with the founder to estimate effort, writing proposals, negotiating statements of work.
• Closing: Managing procurement processes, handling contracts, dealing with legal redlines, getting signatures. No sales ops support.

The Honest Reality

What's Hard

• No brand recognition: You're competing against established firms with case studies and references you don't have. Cold outreach response rates will be low.
• Wearing all hats: You're SDR, AE, SE, and sales ops rolled into one. No CRM admin, no proposal templates, no standard process - you're creating everything.
• Inconsistent pipeline: Consulting deals are lumpy. You might close 2 deals one quarter, zero the next. No recurring revenue to smooth things out.
• Technical depth required: You need to credibly engage in conversations about vector databases, model fine-tuning, inference optimization, MLOps tooling. If you can't, you lose credibility fast.
• Founder dependency: Complex deals will likely need the founder's technical expertise. You're scheduling around his availability.

What Success Looks Like

• Booking 5-10 qualified discovery calls per month with companies actively implementing AI
• Closing 1-2 consulting projects per quarter worth $50K+
• Building a repeatable outbound motion that generates consistent pipeline

Who You're Selling To

Primary Buyers:

• VP/Director of Engineering at companies building AI products
• Head of Data Science/ML at mid-size tech companies
• CTOs at Series A-C startups implementing AI features

What They Care About:

• Technical credibility - can you actually understand their architecture and challenges?
• Speed to value - how fast can you help vs hiring in-house or using a big consultancy?
• Specific expertise - do you know their stack (AWS/GCP/Azure, specific ML frameworks)?
• Risk mitigation - what if the project goes sideways with a 2-person firm?

Requirements

• 2-4 years in technical sales, sales engineering, or solutions consulting (ideally in AI/ML tooling, cloud infrastructure, or data platforms)
• Ability to understand and discuss AI infrastructure: model training, deployment pipelines, vector databases, LLM fine-tuning, inference optimization
• Comfortable with 100% self-sourced pipeline - you've done outbound before and know how to generate meetings from scratch
• Experience selling consulting/services (not just SaaS) - you understand project-based sales cycles, SOWs, and value-based pricing
• High tolerance for ambiguity and building processes from zero
• Self-sufficient - no sales manager to coach you, no enablement team, no established playbook