What is RJ Lindelof's experience with remote teams?

RJ has run hybrid onsite and remote teams since 2006 and fully distributed global engineering teams since 2012, with onshore, nearshore, and offshore engineers across the United States, Ukraine and wider Eastern Europe, Germany, India, China, and the Philippines - run as one engineering organization, not a vendor at the far end of a ticket queue. He understands the tools, communication patterns, and cultural dynamics required for high-performing remote engineering organizations and excels at managing FTEs, contractors, and strategic partners across multiple time zones.

What size teams has RJ Lindelof managed?

RJ has managed engineering organizations from 10 to 175+ engineers at peak, and scaled Successware from 30 to 175+ with 80% of that growth in a single nine-month window, spanning onshore, nearshore, and offshore direct reports, contractors, and strategic partners. He has experience scaling teams rapidly while maintaining quality, culture, and delivery velocity. His teams consistently achieve 99.95% uptime and ship features with fewer defects.

What is RJ Lindelof's approach to AI in software development?

RJ advocates for an AI-native SDLC where artificial intelligence is embedded throughout the development lifecycle as production infrastructure, not a productivity add-on. At GlobalMed he pioneered Claude Code adoption in May 2025 with guardrails and usage policies for regulated delivery and cut new-developer time-to-productive 35%; at hc1 the AI-native pipeline delivered 5x deploy frequency and a 23% PR throughput gain; at SteadyIQ he cut LLM inference cost 90% by right-sizing the workload onto low-cost and no-cost models. He has delivered AI-powered features in regulated environments across govtech, fintech, and healthtech, and focuses on responsible AI implementation with proper safeguards, ethical guidelines, and compliance measures.

What cloud platforms does RJ Lindelof have experience with?

RJ has extensive experience with AWS and Azure, having led multiple cloud-native transformations. He has rebuilt brittle monoliths into distributed systems, implemented auto-scaling infrastructure, and achieved 99.95% uptime at scale. His cloud expertise includes serverless architectures, containerization, and multi-region deployments.

How does RJ Lindelof measure engineering success?

RJ uses a balanced, outcome-focused framework: delivery performance (deployment frequency, lead time, change failure rate, MTTR), code quality and system reliability, team health and developer experience, and business impact metrics like feature adoption, uptime SLAs, and technical debt reduction. He believes in data-driven leadership that aligns engineering outcomes with business goals.

RJ Lindelof | Executive Summary | Engineering Leadership

Executive Summary

RJ Lindelof drives technology strategy, platform scalability, and AI adoption. He has led engineering organizations at PE- and venture-backed companies and mid-market enterprises, in close partnership with product, where he scaled multi-tenant SaaS platforms, put agentic AI systems into production, and modernized cloud infrastructure across AWS and Azure. His work centers on embedding automation, performance, and reliability into high-growth environments. He has a track record of aligning engineering execution with business strategy to accelerate delivery, reduce infrastructure cost, and deliver measurable value to customers and investors alike.

Today he is Senior Director of Engineering, AI & Automation at SteadyIQ, a venture-backed income and employment verification SaaS, where he owns engineering execution and AI/automation strategy and reports to the CPO as part of the executive leadership team. SteadyIQ's Income Passport product turns messy bank-transaction and payroll data into a clean, verified, human-readable income report. The model is B2B2C - SteadyIQ sells to US state government agencies (Missouri, Florida, Hawaii), and those agencies' caseworkers and benefit applicants use it, so people qualify for SNAP, Temporary Assistance, and Medicaid without hunting down paystubs and bank statements. He leads the platform rewrite from a legacy .NET application to a TypeScript/Node/Python monorepo, which he is driving to feature parity - Next.js, React, and Tailwind 4 on the frontend; tRPC, better-auth, Prisma, and Supabase on the backend; Google Vertex AI Gemini behind a custom failover and circuit-breaker layer for income grouping, classification, and name-consistency checks; Plaid and Argyle supplying bank-transaction and payroll/gig income data; PostHog for A/B testing, feature flags, and error observability; Resend for mail; and Twilio for SMS. With the Data Science team, he profiled the workload, found it drawing under 2% of the frontier capability it was paying 100% for, and moved it onto low-cost and no-cost models - cutting LLM inference cost 90% - and he designed and architected document OCR in house, with a self-hosted vLLM (Qwen) fallback, instead of buying it. He built a sprint dashboard that tracks story-to-prod cycle time, escaped defects, release frequency, the percentage of stories shipped with AI-assisted tests, and engineering cost per shipped feature; moved the team to 1-week sprints to align with customer needs, under a lightweight process that raises delivery visibility and sharpens velocity; and holds the Eastern Europe offshore contracting team to the same quality and delivery standards as staff, run as one engineering organization. Regulatory change keeps the eligibility rules moving, so the platform has to move with them - all under HIPAA, SOC 2, and ISO 27001.

At hc1, he was Senior Director of Software Engineering and led the full SaaS engineering org through an AI-native transformation across a multi-language platform (Java, C#, Python), ingestion pipelines, data lake, post-merger integration, and two flagship initiatives anchored in agentic AI. He drove the org from "AI-curious" to "AI-proficient" in deliberate waves - engineering and data first, then DevOps, Integrations, Professional Services, Product, and HR - tracked prompt throughput as a leading indicator alongside delivery and quality metrics, and ran hc1's first AI Hackathon with rolling Show & Tell sessions that turned individual experiments into shared playbooks. Underneath it he built the foundation for AI governance in a regulated environment - multi-model policies, prompt-use frameworks, and code-assist tool boundaries across the SDLC - while preserving HIPAA and SOC 2 posture. He cross-trained Product on Playwright for smoke and release-regression tests, and paired an AI-assisted documentation refresh with a lead-mentor program to cut new-engineer onboarding time 70%. Partnering with the CPO on FY26 product strategy, his Source IQ gap analysis drove a three-phase roadmap. Source IQ was a greenfield agentic supply chain intelligence platform combining contract performance with utilization analytics on Python FastAPI and vLLM (Qwen3.6-27B), consolidated late-stage from a hybrid Java + Python stack to remove a service hop, with production AI contract extraction and compliance scoring. Clinical IQ was a clinical intelligence SaaS with direct Epic EMR integration via HL7/FHIR on HIPAA-compliant AWS, surfacing AI-detected patterns and lab/test recommendations to close care gaps and support earlier intervention.

Over 20 years of engineering leadership across GovTech, FinTech, HealthTech, EdTech, and regulated B2B SaaS - at PE-backed, venture-backed, bootstrapped, and mid-market enterprise companies, running organizations from 10 to 175+ engineers. He has run hybrid teams since 2006 and fully distributed global teams since 2012 - onshore, nearshore, and offshore engineers across the United States, Ukraine and wider Eastern Europe, Germany, India, China, and the Philippines - as one engineering organization, not a vendor at the far end of a ticket queue. At Successware (PE-backed), he scaled the engineering org from 30 to 175+ engineers over his tenure across onshore, nearshore, and offshore - 80% of that growth in a single nine-month window - with 15+ Senior Managers and Directors reporting in, delivering 99.95% SLA at sub-second response for 10k+ concurrent users on a re-architected, multi-tenant AWS-native platform. He drove FinTech revenue expansion there through GraphQL and AI-enabled APIs that accelerated merchant onboarding and unlocked new integration partnerships, and from 2021 to 2024 pushed offshore partners to adopt generative AI for DevOps automation, performance-test generation, MVP scaffolding, and synthetic user creation - a measurable productivity multiplier across vendors.

At GlobalMed - founder-owned, privately held telehealth technology powering the VA and White House Medical Unit - he owned engineering for the SaaS portfolio, including roadmap, budget, and delivery, with 4 Senior Managers and 30 engineers across onshore and offshore teams. He led the company-wide AI Taskforce spanning Engineering, Product, QA, Operations, HR, and Finance, built an enterprise AI literacy program that made every employee "AI-capable" as a baseline competency, and established a Confluence AI Knowledge Hub, cross-functional Innovation Sprints, and a shared Prompt Library that surfaced production-ready workflows from frontline staff. He pioneered Claude Code adoption in May 2025 - authoring the guardrails, usage policies, and decision frameworks for AI coding assistants in regulated delivery - added OpenAI Codex in June 2025 as a complementary tool with Tuesday Technical Talks codifying when to use which, and cut time-to-productive for new developers 35%. He drove a .NET 8 platform rebuild and Azure cloud modernization that retired 65% of technical debt and lifted deployment frequency 25%, and partnered with the vCISO to close 95% of critical vulnerabilities under HIPAA, SOC 2, and ISO 27001 posture.

Earlier, as Head of Software Engineering at Ruffalo Noel Levitz (2019-2021), he owned the engineering org behind the company's mission-critical HigherEd fundraising SaaS - 5 Senior Managers and 25+ engineers across distributed locations - lifting system throughput 25% and team velocity 30%, cutting incident frequency 40% through observability, environment parity, and release confidence, and modernizing the Java/Bootstrap platform so UI support tickets dropped 85% and defect escape rate dropped 40%; he also replaced legacy telephony with a WebRTC infrastructure that cut communications cost 30% and added integrated payments, messaging, and video. At Debt Pay Pro (2018-2019), a bootstrapped FinTech, he joined as employee #38 to own engineering with 10+ direct reports, led a zero-downtime data center to AWS migration (EC2, MariaDB) coordinating 30+ vendor, payment-gateway, and finance integrations, and cut pull-request merge time 90% with modern branching, CI, and review practices. At Follett School Solutions (2012-2018) he founded and ran the internal R&D startup chartered to accelerate K-12 product innovation - 10+ direct reports across engineering, UX, and QA, overseeing 25+ staff - leading a fully distributed global team from 2012 across McHenry, IL; Greenville, SC; Minneapolis, MN; Hingham, MA; and offshore engineers in India, and standing up the company's first true CI/CD practice under Scrum, Kanban, Lean, SAFe, and Scrumban tuned to each team. Before that, as Senior Software Engineering Manager at Follett Learning (2006-2012), he provided technical leadership across 6 independent SaaS products with 25+ senior developers, grew the org 38%, led hybrid onsite and remote teams from 2006, and led technical due diligence and post-close integration on multiple acquisitions.

His leadership in PE-backed environments includes 30-60 day post-acquisition technology assessments, value creation roadmaps tied to EBITDA impact, and board-ready reporting that connects engineering investment to business outcomes. He has presented architecture and investment cases directly to PE advisors and the C-suite, has owned full P&L for engineering organizations including budget and compensation, and has driven post-merger integration including portfolio rationalization and organizational alignment to right-size combined orgs.

RJ architects for longevity, not flash. He embeds CI/CD pipelines, enforces quality gates with SonarQube and GitHub Actions, and drives velocity with clear standards and metrics. At hc1, he built a "Quality as Accountability" model with three-step testing (Smoke, Targeted, Full Regression) using Playwright and GitHub Actions quality gates - no dedicated QA team required. Engineering, Product, and Services shared release accountability; AI-assisted test generation lifted code coverage from under 10% to 40%. See how he measures engineering success.

AI is not layered on. It is built in. At hc1, GitHub Actions, Claude Code, Copilot, and AWS Kiro drove code generation, test synthesis, cloud architecture scaffolding, and documentation as first-class CI/CD pipeline stages. The toolchain was multi-model by design - Kiro, Claude, Copilot, Gemini, Snowflake Cortex - with no single-vendor dependency. The results: 5x deploy frequency, 23% PR throughput gain, and a 70% reduction in new-engineer onboarding time. Production agentic workflows used MCP and A2A protocols; AI governance ran on agent SLOs and audit trails. He carries the same multi-model, no-single-vendor discipline into SteadyIQ's AI-native SDLC. Learn more about his AI integration approach.

He leads with clarity, challenges assumptions, and builds high-performing, globally distributed teams that own their outcomes. His engineering cultures run on trust, autonomy, and accountability - because those are the conditions where high-impact work gets done. Personal standards set the bar - uncompromising attention to detail at the architecture level, the PR level, and the post-mortem level. Explore his detailed leadership philosophy.

RJ holds a Bachelor of Science in Computer Science from Columbia College Chicago and an Associate in Applied Science in Computer Information Systems from McHenry County College. His certifications include DataCamp (AI Governance, Understanding AI) and Anthropic (Claude Agent Skills, Claude Subagents, Advanced Model Context Protocol, and Claude Integrations).

RJ is also available for fractional CTO and interim technology leadership engagements. Whether you need a full-time leader or a strategic advisor, he delivers. Every time.