AI/ML ENGINEER · APPLIED AI SYSTEMS
AI/ML Engineer | Applied AI Systems | Generative AI, RAG, Agentic AI, MLOps & AWS
Evidence before complexity
ABOUT / PROFESSIONAL PROFILE
I am an AI/ML Engineer and Professional Member of BCS, The Chartered Institute for IT (MBCS), focused on designing and building production-oriented AI systems across machine learning, Generative AI, Retrieval-Augmented Generation, Agentic AI, multimodal AI, and MLOps.
PROFESSIONAL MEMBERSHIP
ACTIVEProfessional Member of BCS, The Chartered Institute for IT, and entitled to use the MBCS post-nominal. The membership reflects a commitment to recognised professional standards, ethical practice, the BCS Code of Conduct, and continuing professional development in computing and technology.
01 / PROFESSIONAL EXPERIENCE
Hands-on work across machine learning systems, RAG, agentic AI, APIs, cloud deployment, evaluation, and MLOps, supported by postgraduate qualifications and professional analytical problem-solving experience.
Designing and building production-oriented AI/ML systems across machine learning, RAG, agentic workflows, FastAPI services, Docker, AWS cloud deployment, evaluation, monitoring, and MLOps.
Postgraduate study and technical development supporting applied AI engineering, technology management, business understanding, cloud systems, and production-oriented solution design.
02 / TECHNICAL CAPABILITIES
Skills and technologies used across applied machine learning, Generative AI, agentic systems, APIs, infrastructure, cloud environments, evaluation, and MLOps.
03 / ENGINEERING WORK
A concise selection of flagship and focused projects across applied AI, machine learning, model serving, and cloud engineering.
FLAGSHIP CASE STUDY
A production-oriented enterprise decision intelligence system combining Retrieval-Augmented Generation, multi-agent workflows, demand forecasting, replenishment recommendations, APIs, a React/Next.js frontend, event-driven processing, orchestration, monitoring, testing, and deployment-oriented infrastructure.
IMPLEMENTATION EVIDENCEValidated local end-to-end workflow integrating RAG, multi-agent orchestration, forecasting, recommendations, API, and frontend.
A reproducible IMDb sentiment workflow comparing TF-IDF Logistic Regression with a 1D CNN and serving the selected Logistic Regression artifacts through FastAPI.
STACKPython · FastAPI · scikit-learn · TensorFlow · pytest · Docker · GitHub ActionsA compact CNN workflow with deterministic CIFAR-10 subsets, tracked evaluation artifacts, validated model loading, Gradio inference, and automated checks.
STACKPython · TensorFlow/Keras · NumPy · Gradio · pytest · Docker · GitHub ActionsA containerized FastAPI deployment lab covering ECR, ECS/Fargate, IAM-based S3 access, CloudWatch logging and alarms, SNS, and ECS task replacement.
STACKPython · FastAPI · Docker · Amazon ECR · ECS/Fargate · IAM · S3 · CloudWatch · SNS04 / OPEN-SOURCE FRAMEWORK
An independently created framework for structuring real-data-first, baseline-driven, evidence-based, and production-aware AI/ML engineering work.
INDEPENDENT OPEN-SOURCE FRAMEWORK
Versioned framework guidance covering problem definition, data validation, baseline development, evaluation, delivery, production planning, and maintenance.
05 / EDUCATION & CREDENTIALS
Formal study across data science, business, computer science, and multidisciplinary science—supported by selected IBM and Google online credentials.
View Education & Credentials →06 / CONTACT
AI/ML Engineer | Applied AI Systems | Generative AI, RAG, Agentic AI, MLOps & AWS
Based in Sri Lanka · Open to international opportunities