Director of Platform Engineering

Title: Director of Platform Engineering

Location: Remote (US Preferred)

Reports to: Head of Engineering

Team Size: 5–10 initially, scaling to 15

Critical Hire Timeline: Week 2–3 (platform foundation required for Maverick launch)

Position Overview

We are seeking an exceptional Director of Platform Engineering to serve as a strategic partner to the Head of Engineering and drive technical excellence, operational efficiency, and business impact across the entire engineering organization.

This is a dual-mandate leadership role that combines:

• Platform & Infrastructure ownership
• Engineering Operations leadership
• Strategic business partnership
• Organizational excellence and execution rigor

This role goes far beyond traditional DevOps or Platform leadership. You will be the right hand to the Head of Engineering, owning day-to-day operational excellence while enabling product velocity, AI scalability, and enterprise-grade reliability.

Role Scope & Accountability

Area

Ownership

Platform & Infrastructure

40%

Engineering Operations

30%

Strategic Business Partnership

20%

Organizational Excellence

10%

Leadership & Collaboration Expectations (Non-Negotiable)

• Resolve disagreements privately, present aligned positions publicly
• No unaligned executive escalations
• Transparent risk communication with aligned mitigation plans
• Platform team must be viewed as an enabler, not a blocker
• Influence through trust, data, and partnership, not authority

Key Responsibilities

I. Platform & Infrastructure Leadership (40%)

Cloud, Architecture & Scalability

• Own AWS infrastructure strategy (EKS, RDS, VPC, IAM, networking)
• Define multi-tenant SaaS patterns (shared DB RLS, silo for enterprise)
• Scale platform from 10 → 100 → 500 customers
• Drive vendor evaluation and build vs. buy decisions
• Ensure reliability, performance, security, and cost efficiency

AI / ML Infrastructure & MLOps (Critical)

Self-Hosted LLM Infrastructure

• Deploy and operate self-hosted SLMs for privacy and cost efficiency
• GPU infrastructure (AWS P4/G5, 8–16 GPUs)
• Model serving: vLLM, TGI, Ray Serve
• Fine-tuning pipelines (LoRA, QLoRA)
• Quantization (4-bit / 8-bit) and autoscaling

Model Deployment & APIs

• Deploy predictive ML models (forecasting, classification, anomaly detection)
• Real-time inference (<100ms p95) and batch pipelines
• CI/CD for models with canary and blue-green deployments
• Drift detection, accuracy tracking, rollback

AI Cost Management & Pricing Enablement

• Token and GPU cost tracking per tenant and feature
• Unit economics for AI workloads
• API vs self-hosted break-even modeling
• Prompt caching, response caching, batching strategies (40–60% savings)

AI Observability & SLOs

• LLM latency (p50/p95/p99), success rates, token usage
• Agent performance (completion rate, tool success, latency)
• RAG quality metrics and retrieval accuracy
• Cost anomaly detection and alerting

DevOps, Reliability & SRE

• Build and scale DevOps/SRE team (3–4 → 8–10)
• CI/CD with <10 min deploys, GitOps (ArgoCD / Flux)
• Define SLAs/SLOs (99.9% uptime target)
• Incident response, blameless postmortems, MTTR/MTTD tracking
• Disaster recovery and business continuity planning

Security & Compliance

• Own SOC 2 Type I & II, GDPR, HIPAA readiness
• Zero Trust security architecture
• Vulnerability management and pen testing
• Security team hiring and security champions program
• Incident response and forensics

Cloud Cost Optimization (FinOps)

• Own AWS AI budget ($50K → $500K /month)
• Reserved instances, spot strategies, right-sizing
• Cost allocation by tenant and team
• Target: 20% YoY cost reduction

II. Engineering Operations Leadership (30%)

Talent & People Operations

• Hiring strategy for 33–44 engineers in Year 1
• Build offshore development centers (India / Eastern Europe)
• Own performance reviews, promotions, PIPs, exits
• Define career ladders, leveling, and compensation bands
• Coach managers and directors

Engineering Productivity & Tools

• Own dev tooling: GitHub, CI/CD, Jira/Linear, Notion, Datadog
• Track DORA metrics, cycle time, developer NPS
• Reduce friction via automation and internal platforms
• Vendor management and SaaS consolidation

Process & Execution Excellence

• Agile ceremonies, RFCs, architecture reviews
• Release management and dependency coordination
• Executive dashboards and KPI reporting
• Conflict resolution via private alignment and consensus

III. Strategic Business Partner (20%)

Platform & AI Pricing Strategy

• Define SaaS AI pricing tiers (Starter / Pro / Enterprise)
• Usage-based AI pricing (queries, tokens, agents)
• Gross margin modeling (>70% infra, >60% AI)
• Cost-to-serve and break-even analysis

Financial Planning & Advisory

• Engineering budget ownership ($4.7M–$6M)
• Headcount and infrastructure forecasting
• ROI analysis for infrastructure investments
• Vendor negotiation (AWS, Datadog, Auth0, LLM providers)

Strategic Leadership

• Identify blindspots proactively
• Quarterly and annual planning partner to Head of Engineering
• Support Sales on enterprise deals and security reviews
• Board and investor-facing technical leadership

IV. Organizational Excellence (10%)

• Define and reinforce engineering culture and values
• Knowledge management, documentation, onboarding playbooks
• Executive communication and board-level reporting
• High-trust, high-performance environment

Required Qualifications

Technical

• 12 years engineering experience, 6 years leadership
• AWS at scale (EKS, RDS, VPC, IAM)
• Kubernetes, Terraform, CI/CD, DevSecOps
• Required: Self-hosted LLMs, GPU infra, MLOps in production
• AI cost optimization and observability experience
• Security and compliance leadership (SOC 2, GDPR, HIPAA)

Operational & Business

• Led 30–50 person engineering orgs
• Hiring, performance management, and org design
• $5M engineering budgets
• SaaS unit economics and pricing strategy
• Executive-level communication and diplomacy

Preferred Qualifications

• VP Engineering experience at Series A/B/C startup
• Large-scale AI/GPU deployments (100 GPUs)
• Fintech or regulated domain experience
• Offshore center build-out experience
• MBA or executive leadership training