About RJ Lindelof

A 20+ year senior engineering leader recognized for driving transformative business outcomes through high-growth SaaS platforms, cloud-native migrations, and production-scale agentic AI adoption. Strongest in HealthTech and EdTech SaaS; at home in regulated B2B - most recently operationalized agentic AI across the full software lifecycle, where pilots became production infrastructure, not demos. Read the case study →

Target profile: Head of Engineering, VP of Engineering, CTO at growth-stage companies under 250 people, ideally with engineering teams of 10 to 60. Sweet spot: 10-60 engineers at the inflection point where engineering becomes a strategic asset, not a cost center. I scale teams thoughtfully (10 to 30, 30 to 50) without breaking culture, and have taken an org past 175 at 99.95% SLA when the business demanded it - but my preferred range is where every hire and architectural decision has direct impact.

My strategic focus is AI-native SDLC as production infrastructure. I run a multi-model strategy across Claude Code, GitHub Copilot, AWS Kiro, OpenAI Codex, Gemini, Bolt, and Snowflake Cortex, with frontier open-weight models on vLLM for greenfield agentic platforms. 13 years as an IC before management - Java, JavaScript, Node, .NET, Delphi, C++ - and I still spin up local dev environments and prototype alongside my teams. That technical credibility is how I earn the right to push senior engineers and architects. Player-coach means PR review, architectural review, platform modernization, and process design - not 50% feature delivery. I am past the career stage where coding tests are useful screening signal.

Outcomes that compound: 5x deploy frequency, 23% PR throughput gain, test coverage lifted from under 10% to 40% with no dedicated QA team, new-engineer onboarding cut 70%, MTTR cut 30%, all at 99.95% SLA. HIPAA, SOC 2, ISO 27001, HL7/FHIR, and Epic EMR integration shipped - not promised.

How I actually spend my week. I am not a 40-hour-a-week individual contributor anymore. I review pull requests, run architecture reviews, and challenge senior engineers on design decisions. The coding I do myself is mostly to identify and automate the repetitive work across the company - not just my own inbox. I analyze a team's workflows and find the work that should be automated; sometimes that is a one-time script or internal tool I code, sometimes a custom MCP server, sometimes no-code or low-code automation on Claude CoWork, Computer Use, OpenAI Codex, or Zapier. I stay current on frontier AIs - evaluations, justifications, where each model earns its keep - and the highest-leverage thing I do for a company is the judgment call on where AI makes sense and where it doesn't. That call is sound and rock-solid because I have been writing software for two decades and leading engineers for fifteen years; I know what AI is good at, what it is not, and how to keep teams shipping without losing their judgment to it.

Quality is just as important as shipping. A release that lands on time but does not actually work is not a win - it is a cleanup project handed to the next sprint. AI now adds real value here: automated unit-test generation, Playwright-driven UI regression suites, AI-assisted PR review, and SonarQube quality gates in CI/CD mean we ship fast and ship quality - not one or the other.

Cross-functional AI partnership. Engineering is not the only place AI shows up. I have partnered directly with CPOs on Lovable and jointly with product teams on Bolt to ship rapid prototypes that engineering then hardens for production - extending AI tooling beyond the engineering org while keeping a clean handoff into the SDLC.

Open to a full-time senior engineering leadership role (VP, SVP, CTO, Senior Director of Engineering, or Head of Engineering). Servant leader. Player-coach. Hire for curiosity and aptitude. Build psychological safety so teams will challenge ideas, admit mistakes, and ask the questions that prevent the next outage. See all engagement options on the contact page.