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AI Engineer with 2+ years in scalable data engineering and machine learning.
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Assessing your cultural and operational fit
Second-year AI and Data Science student bridging scalable data engineering with advanced machine learning. Proficient in Python, PySpark, and deep learning, with proven experience building LLM-driven RAG applications and multimodal chatbots. Backed by AWS Cloud Practitioner training, I am seeking an internship to deploy production-ready AI systems and engineer robust data pipelines.
Informatics Institute of Technology
BSc (Hons) · Artificial Intelligence and Data Science
August 1, 2024 – Present
Informatics Institute of Technology
Foundation Certificate · Higher Education
August 1, 2023 – June 30, 2024
DocIntel: Multimodal RAG System
June 27, 2026 – Present
Engineered a production RAG application using Groq Llama 3.3 for natural language Q&A across PDFs and images, directly contributing to AI capability building. Designed a retrieval pipeline with Hugging Face SBert embeddings and cross-encoder reranking, validated by Precision@K and MRR metrics. Architected an AWS S3 ingestion workflow with layout-aware OCR, feeding a FAISS and Dockerized OpenSearch dual-vector backend.
EmotiChat: Multimodal Emotion-Aware AI
June 27, 2026 – Present
Engineered a real-time multimodal AI assistant (Groq Llama 3.3) integrating speech (Whisper), vision (DeepFace), and NLP (DistilRoBERTa) for continuous emotion detection. Developed an asynchronous FastAPI pipeline utilizing librosa and custom weighted fusion for high-confidence emotional baselines with zero UI latency. Architected a secure AWS DynamoDB data workflow to log interactions, powering an interactive Emotion Tracker dashboard with Plotly analytics.
F1 Cloud Data Pipeline – Predictive Analytics
June 27, 2026 – Present
Built a Boto3 ingestion script to load decades of Formula 1 data into an S3 Data Lake, using event-driven AWS Lambda for pre-ETL validation. Developed a distributed PySpark ETL workflow via AWS Glue for large-scale data transformation, imputation, and predictive feature engineering. Orchestrated an automated AWS Step Functions execution flow routing transformed data to year-partitioned Parquet sinks for ML inference.
Toura.lk: Tourism Intelligence System (Recommendation model)
June 27, 2026 – Present
Engineered a hybrid ML system using three sub-models for context-aware, proximity-based destination and service recommendations. Architected an AI pipeline combining SBERT embeddings, BERT sentiment analysis, and GNNs for spatial modeling. Implemented LightGBM LambdaMART for Learning-to-Rank, maximizing NDCG@5 across an enriched dataset of 35,000 tourist reviews.
Introduction to Prompt Engineering for Generative AI
January 1, 2026 – Present
AWS Cloud Practitioner
Udemy
December 1, 2025 – March 1, 2026
Cultural Fit Analysis
The candidate's project diversity, ranging from multimodal RAG systems to predictive analytics and recommendation engines, demonstrates a broad interest and adaptability, which aligns well with an innovative culture. The involvement in volunteering and extracurricular activities suggests a well-rounded individual with leadership potential and a collaborative spirit. The focus on building production-ready AI systems and robust data pipelines aligns directly with the target role of an AI Engineer.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as effective communication, collaborative teamwork, time management, problem-solving, public speaking, adaptability, and leadership. These indicate a good operational fit for a team-oriented and dynamic AI engineering role. The project descriptions suggest an ability to work independently and deliver complex solutions.