Senior Manager - New Business (POD Leader)

Overview

You're selling AI infrastructure and deployment platform to enterprise technical buyers—CTOs, VPs of Engineering, and AI/ML leads at companies building production AI systems. You own everything from prospecting to close, managing 8-12 active opportunities while also leading a small pod of AEs or SDRs. The product is complex (gateways, model deployment, agent orchestration) so your deals involve technical validation, security reviews, and procurement.

Role Snapshot

Company Context

Stage: Series A (backed by Intel Capital and Sequoia/Peak XV Partners)

Size: ~100 employees

Growth: Active enterprise customer base includes Automation Anywhere, Resmed, Siemens Healthineers

Market Position: Infrastructure player in crowded AI tooling space - competing on governance, security, and production-grade deployment vs point solutions

GTM Reality

Pipeline Sources:

• 70% Outbound - You're identifying companies building AI/agentic systems and cold reaching out to engineering leaders
• 20% Inbound - Some leads from product-led signups, webinars, or existing customer referrals
• 10% Partners/Referrals - Cloud marketplace deals or integration partner referrals

SDR/AE Structure: Likely building this out - you may have SDR support but expect to do significant self-sourcing early on

SE Support: Solution engineers help with demos and technical validation, but you need to understand the architecture yourself

Competitive Landscape

Main Competitors: Likely competing against AWS SageMaker, Azure ML, GCP Vertex AI (hyperscalers), plus point solutions for LLM gateways, agent frameworks, or MLOps platforms

How They Differentiate: Enterprise governance layer - RBAC, audit logging, compliance-ready architecture. Unified platform vs stitching together multiple tools. Cost optimization through GPU utilization.

Common Objections: "We can build this ourselves," "Why not just use AWS/Azure/GCP," "Too early stage for us," pricing concerns

Win Themes: Speed to production, security/compliance requirements, multi-cloud flexibility, cost control

What You'll Actually Do

Time Breakdown

Prospecting & Outreach (25%) | Active Deal Management (40%) | Internal/Team Leadership (20%) | Admin/Forecasting (15%)

Key Activities

• Identifying Target Accounts: You research companies in TMT, Healthcare, BFSI, High-Tech that are building AI products. You're looking for engineering orgs of 50+ people with AI initiatives. You build lists, find the right contacts, and figure out if they have budget.
• Cold Outreach to Technical Buyers: You send LinkedIn messages and emails to VPs of Engineering, CTOs, Head of AI/ML. Most ignore you. You're trying to get 3-5 initial meetings per week. You need to speak their language - you're not selling features, you're discussing their architecture.
• Running Technical Sales Cycles: Your deals have 4-6 stakeholders. You do discovery with engineering, demo with architects, build business case with operations, handle security questionnaires with InfoSec, negotiate with procurement. You spend a lot of time coordinating internal resources and chasing next steps.
• Managing Your Pod: You're coaching 2-4 other AEs or SDRs. Weekly pipeline reviews, deal strategy sessions, helping them with stuck deals. You're also expected to model good selling behavior.

The Honest Reality

What's Hard

• Most enterprises you cold call already have infrastructure investments. You're displacing existing tools or internal builds, which means longer evaluation cycles and "not now" responses.
• Technical sales to engineers means they'll test your knowledge. You need to understand LLM deployment, GPU optimization, agent frameworks, and security architecture. Fake it and you lose credibility fast.
• Deals slip constantly. Security reviews take 6 weeks. Procurement wants three vendors. Budget gets frozen. You'll have 60-70% of your pipeline push each quarter.
• You're player-coach, so you're carrying quota while also managing people. Tension between your own deals and coaching time.
• Series A means some things aren't built yet. You'll hear "does it do X?" and the answer is "roadmap" more than you'd like.

What Success Looks Like

• You close 4-6 new logo deals per year, each $100K-300K ACV
• Your pod hits 80%+ of team quota consistently
• You build repeatable playbooks for targeting specific verticals (healthcare AI, fintech, etc.)

Who You're Selling To

Primary Buyers:

• VP Engineering / CTO (final budget authority)
• Head of AI/ML or Data Science (technical champion)
• Engineering Manager building AI products (day-to-day user)

What They Care About:

• Speed to production - can their team deploy models faster than current setup?
• Security and compliance - audit logs, RBAC, SOC2, data residency requirements
• Cost efficiency - GPU utilization rates, cloud spend optimization
• Avoiding vendor lock-in - multi-cloud, flexibility to change models/frameworks
• Integration with existing stack - CI/CD, monitoring, data pipelines

Requirements

• 8-10 years in B2B SaaS sales, at least 3-4 years selling to technical buyers (DevOps, infrastructure, data/AI tools)
• Track record of consistently exceeding quota and closing $100K+ deals
• Experience in at least one of: TMT, Healthcare, BFSI, or High-Tech verticals
• Strong enterprise network - you can get warm intros to VPs at target accounts
• Technical enough to discuss architectures, APIs, deployment models without an SE in every call
• Leadership experience - you've mentored or managed other sellers
• Comfort with Series A ambiguity - you're okay building process, not just executing it