$5.5M+
Documented business impact
Across eight internal developer platforms delivered at Epic Games.
KAWALDEEP SINGH
ENGINEERING LEADER UNLOCKING $5.5M+ IMPACT
I help companies improve development productivity by identifying recurring engineering friction and turning it into self-service platforms, decision systems, and AI-native delivery capabilities.
Previously at
5 → 40
Platform organization scaled
Fourteen Epic employees and 26 EPAM engineers across 10+ time zones.
300
Daily active users
For Build Insights, an AI-assisted engineering intelligence platform.
Zero
Regrettable attrition
Maintained through restructuring and industry-wide gaming layoffs.
The problem I solve
Development productivity is rarely a single-tool problem.
It is usually a system of recurring delays, manual handoffs, fragmented signals, and local workarounds. My role is to make that system visible, solve the highest-value constraint, and turn the solution into a capability the organization can reuse.
Engineers work around the system
Teams lose time to environment setup, flaky validation, support channels, release coordination, and repeated manual checks.
Leaders cannot see the pattern
Friction is distributed across teams, so investment decisions are made without a shared view of cost, risk, adoption, or recurring demand.
Point solutions do not become capability
A script may fix one incident. A productized, self-service platform changes how the organization operates.
How I deliver
From engineering friction to durable organizational capability.
This is the operating model I have used across engineering productivity, platform engineering, release systems, and AI-enabled workflows.
Identify friction
Listen to engineers and operators. Find recurring delays, manual coordination, support demand, failure patterns, and hidden work.
Make it visible
Instrument the workflow and establish a shared baseline for cost, reliability, risk, adoption, and business exposure.
Deliver early wins
Solve a high-credibility problem quickly enough to prove the team can improve the system, not merely describe it.
Restore trust
Create clarity around ownership, priorities, delivery commitments, and communication so stakeholders can rely on the function again.
Win investment
Translate technical friction into a quantified business case and secure the budget, people, and executive sponsorship required to scale.
Productize solutions
Convert the successful pattern into a reusable platform, API, workflow, or decision system with clear ownership and service expectations.
Drive adoption
Integrate into existing workflows, partner with users, remove adoption barriers, and measure whether the capability changes behavior.
Scale the team
Build the operating model, leadership structure, distributed delivery capacity, and durable ownership needed for the capability to compound.
The sequence matters. Investment and scale become easier after teams have seen evidence, stakeholders trust delivery, and the first solution has created measurable value.
Transformation stories
What this operating model looks like in practice.
Three chapters show the same pattern: start with recurring friction, establish credibility, create reusable engineering capability, and expand the organizational mandate.
Starting point
Joined during leadership turnover, company-wide layoffs, and a function in flux. Release reliability and stakeholder confidence needed to be re-established.
What I did
Defined reliability targets, instrumented release health, delivered early improvements, secured vendor investment, and productized recurring needs into self-service platforms and engineering intelligence.
Outcome
Scaled the function from 5 to 40 engineers, owned a $2.2M annual investment, maintained zero regrettable attrition, and delivered eight platforms with $5.5M+ documented impact.
Starting point
Pokémon GO lacked mature CI/CD, standardized automation, scalable device validation, and self-service deployment workflows.
What I did
Built GitLab CI/CD and GCP/Kubernetes workflows, introduced pre-merge validation and one-click deployment, conceived the BOM versioning strategy, and established enterprise device validation.
Outcome
Eliminated 8,000+ manual hours annually through the API automation platform, delivered approximately $400K in savings, expanded iOS capacity from 4 to 48 devices, and reduced annual operating cost by $200K+.
Starting point
Agentic tools can accelerate coding, but speed alone does not create decision discipline, production readiness, organizational knowledge, or safe enterprise adoption.
What I did
Built a reusable 10-agent AI Software Factory with minor and major delivery lanes, ADR and Product Decision records, domain experts, quality gates, human approvals, and a decision verifier.
Outcome
Created a production-oriented model for combining agentic delivery with explicit governance, reusable engineering knowledge, and human-controlled quality gates; applied it through Customer Pulse and MealBrain.
Selected impact
Measured outcomes, not activity metrics.
The work spans cost reduction, avoided growth, engineering capacity, reliability, adoption, and decision quality.
~$3M
Cosmetic Validation Platform
Documented savings from nightly, self-service visual validation across 10+ platforms and eight engineering teams at Epic Games.
~$2M
Cost avoided
Self-service multiplayer performance validation helped a 125-person QA organization avoid projected growth to 200 engineers.
46%
Faster CI/CD execution
Modernization across 10 platforms and 14 test suites reclaimed 60,000+ build-agent hours annually and delivered approximately $500K in savings.
300
Daily active users
Build Insights translated code changes into plain-language risk and validation guidance for QA, Release, Engineering, and Technical Art.
~80%
Blame identification accuracy
Reusable internal LLM services achieved approximately 80% blame identification and 70% failure-triage accuracy.
8,000+
Manual hours eliminated
Niantic's API automation framework improved server-side validation confidence and delivered approximately $400K in annual savings.
Career journey
A career built around removing engineering friction.
Microsoft established the engineering foundation. Expeditors created the first automation platform. Niantic expanded the mission into developer productivity. Epic scaled it into engineering transformation. The current chapter extends it into AI-native engineering.
Independent AI-Native Engineering
AI Product & Software Factory Work
Building reusable agentic delivery systems, decision governance, quality gates, domain experts, and human approval workflows.
Epic Games
Engineering Director, Platform Engineering
Led internal developer platforms, AI engineering workflows, engineering intelligence, CI/CD, global team scale, and investment strategy.
Niantic
Founding Lead, Development Productivity & Platform Engineering
Established CI/CD, deployment, automation, device validation, pre-merge workflows, and shared platform versioning for Pokémon GO.
Expeditors International
Automation Lead
Created the organization's first Java-based API and UI automation platform and integrated it into CI/CD for self-service validation.
Microsoft
Software Development Engineer II · Excel and MSN
Delivered cross-platform Excel functionality, reusable APIs, Azure service reliability, and engineering automation across product and service environments.
HCL Technologies
Test Lead · Engineering Validation Lead
Led an eight-engineer team validating the Live@Edu to Office 365 migration across 27 locales.
What I am hired to own
Platform strategy, transformation, and measurable adoption.
I operate at the intersection of technical systems, product thinking, organizational design, and executive investment.
Development Productivity
Find and remove recurring friction across build, test, deployment, release, environment, and support workflows.
Internal Developer Platforms
Turn local solutions into self-service platforms, APIs, paved paths, and reusable engineering capabilities.
Engineering Intelligence
Create shared signals for reliability, release readiness, risk, investment decisions, and engineering effectiveness.
AI-Native Engineering
Integrate LLM and agentic workflows with governance, decision discipline, quality gates, and human control.
Organization & Investment
Build roadmaps, secure executive sponsorship, own budgets and vendors, and scale global engineering teams.
Adoption & Change
Partner with users, earn trust, integrate into existing workflows, measure behavior change, and sustain platform ownership.
Background
Technical depth, executive leadership, and community service.
Master's in Computer Engineering, ongoing agentic AI study, and long-term community leadership alongside engineering work.
Education
Agentic AI Foundations: Business Applications and Risks, Harvard University, July 2026. Master's in Computer Engineering, Punjabi University. Bachelor's in Electrical & Electronics Communication Engineering, Punjab Technical University.
Community leadership
Founder and leader of Har Sewa Foundation, a 501(c)(3) nonprofit, and member of the Bellevue School District CTE Advisory Committee.
Start a conversation
Your engineers already know where the friction is.
The opportunity is to make it visible, solve it credibly, and turn the solution into a capability the organization can scale. I am open to Director and VP-level roles in development productivity, platform engineering, engineering transformation, and AI-native engineering.