Muhammad Anwer
Full-stack engineer focused on building high-impact products: from rapid 0→1 MVPs to scalable, production systems. I combine product thinking, strong systems design, and execution speed to deliver meaningful results.
I strongly believe in iterative approaches: identify a problem, prototype a solution, test it with real users, build/pivot accordingly. AI is part of that loop end-to-end, from scoping to development and beyond.
Recent Wins
Guest checkout, shipped
End-to-end at LaunchGood. >50% of new subscribers adopted it, lifting conversions ~40%. Staple feature for onboarding and acquisition ever since.
MVP wins demo
Built and led an emissions calculation platform from prototype to production, helping client win Desjardins demo.
Get Better Together
Designed and shipped a social fitness app end-to-end using AI agents featuring leaderboards, user auth and an anonymous guest mode that lets users join challenges in seconds.
Core Competencies
Languages
TypeScript, JavaScript, Python, SQL
Frameworks & Libraries
React, Redux, NestJS, Next.js, Vite, Prisma, Tailwind CSS, Jest, React Testing Library, Scikit-learn
Architecture & Systems
Microservices, event-driven systems, RabbitMQ, REST/OpenAPI (contract-first APIs), type-safe clients, cross-service integration
Reliability & Operations
Incident management, production triage and mitigation, root cause analysis (RCA), runbooks, post-incident hardening, on-call rotations, Datadog (logs, monitors)
Cloud & DevOps
AWS, Docker, Git, GitHub Actions, CI/CD, monitoring and alerting
Engineering Leadership
System design (RFCs), design reviews, product/engineering collaboration, analytics instrumentation (Heap), roadmap input, code reviews, mentoring distributed teams
Stakeholder & Delivery
Align non-technical stakeholders, manage upwards, and partner closely with product and design. Translate ambiguous requirements into scoped deliverables, surface tradeoffs early, and communicate risk before it becomes urgent. Comfortable in agile (sprints, standups, planning, retros); have led pilots, vendor demos, and post-incident reviews directly with customers and executives.
AI in Practice
Force multiplier across the full SDLC, used every day. Cursor + agentic coding for feature scaffolding, refactors, and codebase navigation; AI-assisted code review to catch regressions before PR; debugging triage of incidents and stack traces; Production experience with OpenAI, Cursor, Cleric, Mendral, and Langbase; ML foundations in TensorFlow, PyTorch, Scikit-learn.