Fractional Engineering Leadership

Senior Engineering Leadership Without a Full-Time Hire

I help growth-stage companies scale their engineering organizations and cloud infrastructure with senior leadership who ships code, designs systems, and leads teams on a fractional basis.

Why Fractional?

Most growth-stage companies face the same dilemma: you need deep technical leadership at the infrastructure and platform layer, but hiring a full-time senior hire is a 12–24 month commitment at minimum, plus equity, benefits, and recruiting overhead. You're not ready for that headcount yet, but you're also not willing to let your infrastructure become technical debt.

Fractional is the answer. 

Fractional engagement fills that gap. You get 10–15 hours per week of senior engineering leadership who builds and ships – not just advises. Focus areas include: strategic vision, architecture implementation and reviews, team mentoring, cost optimization, and ownership of getting things shipped. The engagement scales up or down as needs change, and you only pay for the time you use.

Book Intro Call

The AI Question

You've heard it everywhere: "AI can write code, debug systems, and design architectures." That's true. But there's a difference between AI helping you build and AI replacing experienced judgment.

What AI Does Well:

  • Generating boilerplate code and Terraform templates
  • Debugging known error patterns
  • Writing documentation and run books
  • Automating routine SDLC tasks

What AI Doesn't Replace:

  • Architectural judgment — Knowing which tradeoffs matter for your stage and constraints
  • Pattern recognition from scale — Having seen what breaks at 10M vs 100M users
  • Vendor/technology evaluation — Choosing tools based on long-term viability, not hype
  • Organizational design — Structuring teams so they move fast without accumulating debt
  • Operational ownership — Accountability for uptime, performance, and reliability when systems run critical workloads

My Approach: I don't compete with AI. I integrate it. I've championed AI-assisted engineering workflows that cut cycle time in production environments. I know where to use AI tools and where human oversight is non-negotiable.

Think of me as your force multiplier -- not a replacement for AI, but the experienced layer that knows when to trust it, when to override it, and what to do when it hallucinates.

I work with your existing stack to define and implement a spec-driven SDLC framework that makes AI a force multiplier, not a liability.

What I Build and Solve

System Design & Software Architecture

  • Full-stack design patterns — Building cohesive stacks where frontend, backend, data pipelines, and infrastructure align around business requirements
  • Microservices architecture — Service boundaries, API design, event-driven patterns, and inter-service communication strategies
  • Data layer architecture — Database selection (SQL/NoSQL), sharding strategies, replication patterns, caching layers, and consistency models
  • Scalability planning — Horizontal vs. vertical scaling tradeoffs, stateless service design, and load distribution
  • Reliability engineering — Fault tolerance, circuit breakers, retry patterns, graceful degradation, and disaster recovery
  • Integration architecture — Connecting internal services with third-party APIs, webhooks, message queues, and data synchronization

Cloud Migration & Architecture

  • End-to-end migrations from on-prem to AWS/GCP
  • Multi-cloud architecture design and governance
  • Infrastructure as Code at scale (thousands of lines of Terraform)
  • Container orchestration and Kubernetes platform setup
  • Identity and Access management frameworks, audits, guidance
  • Cloud topology design, VPCs, policy, service architecture and implementation, WAF, ingress/egress
  • DNS and CDN design, implementation and review

Cost Optimization

  • Cloud billing audits and waste identification
  • Identify cost reductions without sacrificing performance
  • Ongoing cost governance and FinOps practices
  • Budget modeling aligned with growth trajectories

Platform Engineering

  • DevOps pipeline modernization (CI/CD, IaC, observability)
  • Golden paths for developer productivity
  • Environment standardization across teams
  • Release automation and deployment reliability

AI-Native Workflows

  • Engineering SDLC augmentation with LLM tools
  • Code generation and test automation integration
  • Secure, governed AI usage patterns
  • Workflow audits to identify highest-impact adoption areas

Team Scaling

  • Engineering org design and team structures
  • Hiring pipelines and interview frameworks
  • Performance management and career ladders
  • Culture building for high-performing teams

Executive Leadership & Strategic Direction

Beyond the technical layer, you also need someone who understands how engineering fits into the broader business. I help bridge the gap between what engineering is building and what the business needs to achieve.

What This Looks Like:

  • Strategic vision — Aligning technology investments with business objectives and growth trajectory
  • Roadmap planning — Translating business goals into engineering milestones and delivery timelines
  • Product development oversight — Working with product teams to scope, prioritize, and execute feature launches
  • Organizational design — Structuring teams for clarity, accountability, and velocity
  • Executive communication — Presenting technical decisions and tradeoffs to non-technical stakeholders
  • Board and investor updates — Preparing engineering narratives for fundraising, reporting, and stakeholder reviews
  • Cross-functional alignment — Connecting engineering, product, sales, and operations around shared outcomes

Cloud Management

  • Resource provisioning and scaling — autoscaling policies, capacity planning, demand forecasting
  • Cost governance — budget allocation by team/product, chargeback models, ongoing optimization cycles
  • Security baselines — compliance posture monitoring, vulnerability management, patch orchestration
  • Vendor management — negotiating cloud provider credits, SLAs, and enterprise agreements
  • Building out operational playbooks, incident response plans, disaster recovery and operational excellence guidance

Network & Datacenter Infrastructure

  • Global facility footprint design — Planning colocation and multi-region setups with peering, transit, and redundancy baked in from day one
  • Hardware lifecycle management — Sourcing, deployment planning, and decommissioning strategies for servers, switches, and routers
  • Network architecture — BGP/OSPF routing patterns, switching, vpn, firewalls, ids/ips, load balancing, CDN integration, and latency optimization
  • Full lifecycle ownership models — From rack-and-stack planning through cutover, maintenance, and end-of-life disposal
  • Linux/BSD bare-metal deployments — Custom kernel tuning, provisioning workflows, and high-performance computing cluster management
  • HPC Cluster Design — Low-latency interconnects (InfiniBand/RoCE), MPI configurations, distributed filesystems (Lustre/Gluster), and compute node orchestration
  • Capacity engineering — Bandwidth modeling, port utilization planning, and hardware refresh cycles