5+ years
Software engineering experience
Delivery across healthcare, workflow automation, AI products, and platform engineering since 2021.
Software Engineer + AI Engineer
Manoharan Thushantha Sanju
I build reliable product software and AI features that can hold up outside a demo.
My work spans React and Next.js interfaces, Node.js, Python and Java services, delivery automation, and applied AI, with experience across healthcare software, workflow products, and ML-driven applications.
Education
MSc in Artificial Intelligence and Human Factors with Distinction, focused on NLP, reliable prediction, and explainable machine learning.
MSc, Distinction
Artificial Intelligence and Human Factors
Academic work focused on practical machine learning, NLP, and model reliability.
8 flagship builds
Selected portfolio projects
Applied AI builds and full-stack product work with clear engineering context.
Core focus
AI systems, full-stack engineering, cloud delivery
Built with React, Next.js, Node.js, Python, Docker, CI/CD, and transformer-based NLP.
About
Engineering depth across product delivery, AI systems, and reliable release work.
I do my best work where product engineering meets delivery quality. I can move from frontend UI to backend services to CI/CD and AI integration without losing focus on maintainability.
That matters when a team needs someone who can ship across the stack, make sensible technical decisions, and keep the product stable while it is still moving quickly.
Software engineering
I can move from product UI to API design to delivery workflows without losing sight of maintainability.
AI systems
My AI work stays practical: NLP pipelines, transformer models, explainability, and reliability-aware thinking.
Cloud delivery
I have worked on CI/CD, containerised delivery, monitoring, and production deployment instead of treating them as someone else’s job.
Production reliability
I care about how software behaves after release: stability, testing, observability, and real-world usability.
Flagship Projects
Work that shows how I build, not just what I used.
One project leads the story. The others show range across product engineering and applied AI.
Applied AI / Healthcare
Reliable ICD Code Recommendation
Problem
Clinical coding work is high-volume and hard to trust when a model gives predictions without context.
What I built
A transformer-based ICD recommendation workflow with uncertainty-aware output and token-level explanations for review.
Why it matters
Pushed the project beyond raw prediction by focusing on trust, interpretability, and reviewability.
Agent Infrastructure
Scout — Autonomous Research Agent
Built
A research agent built directly on Anthropic native tool use. The full agent loop is ~180 lines of Python: plan → tool-call → observe, with retry budgets, hard-fail thresholds, and three explicit exit paths. No LangGraph, no LangChain — owning the loop is the point.
Result
Tools include Tavily web search, sandboxed `python_exec` (timeout + RLIMIT, no network), URL fetch with byte cap, and per-user `remember`/`recall` memory backed by ChromaDB. Every tool call is a structlog event written to a SQLite trace store and surfaced in a per-run trace viewer. Ships with a versioned eval harness so each release has receipts, not vibes.
Full-stack AI SaaS
CVForge — Structured CV Review Powered by Claude
Built
A CV reviewer tuned for software, ML, and AI engineering roles. Full-stack SaaS with auth, billing, per-tier rate limits, revision history, and a side-by-side compare view. The technical bet: a generic AI reviewer gives bland advice; a role-aware reviewer that knows what an ML engineer’s CV should look like produces dramatically better feedback.
Result
Forces Claude into a single `submit_review` tool call with a strict JSON Schema mirrored by Zod — never parses free-text output, and re-validates at the type boundary so model surprises still get caught. Three-layer prompt architecture (base persona + per-role rubric + user context) so role rubrics can be iterated and snapshot-tested in isolation.
RAG / Retrieval
AskFastAPI — RAG Chatbot Over the FastAPI Docs
Built
A retrieval-augmented chatbot over the entire FastAPI documentation. Hybrid retrieval runs BM25 and semantic search in parallel, fuses with Reciprocal Rank Fusion, then reranks the top 20 down to 5 with a CPU cross-encoder. Markdown-header-aware chunker (~500 tokens, 50-token overlap) preserves the docs hierarchy. Streams Claude’s answer with inline `[N]` citations that link back to the exact section.
Result
25-question eval harness uses Claude-as-judge for faithfulness (structured JSON, 0–1) and parses inline citations against the retrieved set. Every release ships with retrieval@5, citation accuracy, and latency numbers — the eval harness is the headline feature, not the chatbot UI.
Systems / Devtools
Pier — Project Switcher in Rust
Built
A tiny, fast project switcher written in Rust. Replaces the morning ritual of `cd`, `source venv/bin/activate`, `set -a; source .env`, and `docker compose up -d` with a single keystroke. Single-digit-millisecond cold start. Native binary. Zero daemons. First-class support for bash, zsh, and fish.
Result
Solves the cd-from-a-child-process problem the same way zoxide does: emits a shell script on stdout that the wrapper function `eval`s in your current shell. Owns its own `.env` parser so the same file works across all three shells. 36 unit + integration tests, CI with clippy as warnings-as-errors, `thiserror` in the library and `anyhow` in the binary. v0.1.0 released.
Full-stack ML Product
Stock-Crypto Prediction Platform
Built
A MERN and Flask product that combines market views, prediction logic, and recommendation flows in one interface.
Result
Turned a model-driven idea into a usable product flow rather than a standalone experiment.
Product Engineering
Movie Ticket Booking System
Built
A full-stack booking application with JWT authentication and connected browse, select, and checkout flows.
Result
Showed full-stack ownership across auth, user flow design, and connected frontend-backend delivery.
Research-led NLP
Clinical Notes NLP Analyzer
Built
A transformer-based NLP pipeline for entity extraction and structured interpretation of clinical note data.
Result
Extended my healthcare AI work from classification into extraction and structured analysis.
Experience
Roles where I owned delivery, not just isolated tickets.
Short version: what I built, what improved, and where I had real ownership.
Self-employed
Independent Software Engineer
Building and shipping independent full-stack and AI products end to end.
- Own backend services, data models, deployment, monitoring and production fixes across shipped products.
- Design FastAPI and TypeScript service boundaries with PostgreSQL and SQLite data flows, strict schema validation and rate-limited integrations.
- Instrument automated tests, GitHub Actions, structured logs and trace views so failures can be reproduced and fixed quickly.
- Release working products through GitHub with source, deployment notes and technical decisions open for review.
CueCard AI
Software Engineer
Shipped TypeScript applications and Python services for content and workflow automation.
- Built LLM agents with tool calls and evaluation checks into production workflows, validating model output before it reached customers.
- Defined REST and OpenAPI contracts with request validation and idempotency, backed by tested Java and Spring Boot services.
- Implemented streaming workflows that cut content production time by around 50% and increased engagement by around 35%.
- Diagnosed failures with structured logs, monitoring and service health data to help sustain 99.9% uptime through launch.
Bconic
Software Engineer
Shipped a workflow automation platform across Next.js, React, TypeScript, Node.js and Java.
- Reduced customer setup time by around 45% with a visual workflow builder and email automation pipelines.
- Modelled event data in PostgreSQL and built streaming analytics with read models tuned for operational reporting.
- Created reusable validation and integration patterns, protecting releases with pull requests, CI checks and regression tests.
Level 33 Solutions
Software Engineer
Modernised a hospital management system across clinical workflows and backend services.
- Implemented React and TypeScript clinical workflows backed by Java and Spring Boot services over REST and GraphQL.
- Improved p95 latency by around 30% through indexing, query optimisation, caching and asynchronous processing.
- Instrumented validation, correlation IDs and structured logs so production failures could be traced across services.
OREL IT
Trainee Software Engineer
Delivered enterprise application features and owned delivery automation.
- Owned Docker-based CI/CD pipelines with GitHub Actions and Jenkins, reducing deployment time by around 60%.
- Delivered React, Node.js and Express features backed by REST APIs and SQL services.
- Wrote unit, integration and regression tests while services migrated to a modular architecture.
Additional employment
Royal Mail
Postperson
Chapter One
Front of House
Technical Strengths
Capability grouped by real usage context, not arbitrary scores.
Grouped by where I have actually used the tools, not by self-scored percentages.
Languages
Used in production delivery, API work, automation, and AI projects.
Frontend
Used for product interfaces, workflow builders, dashboards, and responsive app shells.
Backend
Used for APIs, service integrations, workflow logic, and distributed application layers.
AI / ML
Used in NLP projects, transformer-based modeling, explainability work, and research builds.
DevOps / Cloud
Used to make releases faster, deployments more reliable, and systems easier to operate.
Databases / Tools
Supporting day-to-day engineering work across applications, automation, and testing.
Research & Education
Research that supports the engineering side of my work.
My academic work strengthens my engineering profile. It is focused on AI that can be explained, evaluated carefully, and used responsibly in real systems.
2025 - 2026
MSc in Artificial Intelligence and Human Factors
Coventry University
Completed with Distinction. Postgraduate work focused on NLP, reliable machine learning, uncertainty-aware prediction, and human-centred AI evaluation.
2020 - 2023
BSc (Hons) Information Technology, Software Engineering
Sri Lanka Institute of Information Technology (SLIIT), Sri Lanka
Built my foundation in software engineering, application development, databases, systems design, and delivery-focused engineering practice.
MSc research focus
Transformer-based NLP and explainable AI methods that keep predictions interpretable and decision-useful.
Publication
Recommendation System for Stock and Cryptocurrency Market Using Cutting Edge Machine Learning Technology
International Research Journal of Innovations in Engineering & Technology (IRJIET) · November 28, 2023
Publication on machine-learning-based recommendation for stock and cryptocurrency market analysis.
Contact
If you need someone who can ship product work and AI delivery, let's talk.
I am open to engineering roles, applied AI work, and product teams that care about quality.