I’m Sedem Quame Amekpewu, a backend-first software engineer with 5+ years of experience building production systems across AI, fintech, mobile, gaming, and developer infrastructure. I help teams turn complex requirements into reliable APIs, automation, and user-facing products, with recent work spanning LLM evaluation pipelines, RAG/RLHF workflows, FastAPI tool-use sandboxes, Flutter/Unity AR apps, USSD/SMS fintech infrastructure, CRM automation, and CI/CD observability.
Clients bring me in when reliability matters: I’ve improved data operations throughput by 30%, supported payment-critical systems at 99.9% uptime, reduced debugging time by 50% for 200+ engineers, and shipped products used by 20k+ mobile users. I work comfortably across Python, FastAPI, Node.js, React, Flutter, Unity, AWS, PostgreSQL, MongoDB, Docker, Kubernetes, Grafana, and OpenAI integrations.
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Built and maintained payment-critical fintech automation across Ghana and Uganda, including SMS, USSD, CRM workflows, localized web experiences, CI/CD, analytics, and observability.
Role: Software Engineer
Stack: React, Node.js, AWS, PostgreSQL, Bitbucket Pipelines, SMS/USSD, CRM integrations
Impact: Supported 99.9% uptime, improved reporting accuracy by 40%, and delivered internal tools used by sales, marketing, finance, and growth teams.
Case study: https://www.twine.net/signin
Delivered Flutter, Unity AR, and backend-connected content workflows for Love Exploring, a cross-platform mobile product serving 20k+ iOS and Android users.
Role: Full-stack Software Developer
Stack: Flutter, Unity AR, Android, iOS, Next.js, Strapi, CocoaPods
Impact: Hardened API/content integration with alternate field mappings, missing-data validation, case-insensitive Unity model matching, release QA, rollout, rollback, and app-store handoff workflows.
Case study: https://www.twine.net/signin
Built enterprise LLM evaluation and delivery workflows across RLHF, SFT, RAG, and tool-use testing. I created realistic FastAPI clones of Slack, Amazon, Jira, and PayPal APIs so agents could be evaluated against production-like workflows before deployment.
Role: AI Engineer / Tech Lead
Stack: Python, FastAPI, PostgreSQL, AWS, Grafana, RLHF, RAG, MCP
Impact: Improved data-prep throughput by 30%, added hallucination/latency/cost observability, and led quality gates for a distributed engineering team.
Case study: https://www.twine.net/signin
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