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MLOps Engineer with less than a year in Machine Learning Operations, Deep Learning, and Generative A
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Assessing your cultural and operational fit
AI/ML Engineer with hands-on experience in Machine Learning Operations (MLOps), Deep Learning, and Generative AI. Proficient in Python, TensorFlow, PyTorch, and production-grade ML deployment using Docker, FastAPI, and CI/CD pipelines. Specialized in Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and agentic AI systems with LangChain and LangGraph. Proven track record of building scalable ML solutions with 95%+ model accuracy and reducing deployment time by 93%.
Sri Lanka Institute of Information Technology (SLIIT)
Bachelor of Science (Honours) · Information Technology with focus on Artificial Intelligence and Machine Learning
August 1, 2022 – Present
Faite
AI/ML Engineer Intern
May 1, 2025 – November 1, 2025
India
Customer Churn Prediction MLOps System
June 1, 2026 – Present
Deployed production-grade Machine Learning REST API handling real-time customer churn predictions with 87% accuracy. Implemented automated CI/CD pipeline using GitHub Actions reducing deployment time from 30 minutes to 2 minutes (93% improvement). Built comprehensive monitoring dashboard using Streamlit tracking 1000+ predictions with data drift detection capabilities. Containerized entire application using Docker achieving 100% reproducibility across development and production environments. Automated testing pipeline with unit tests and integration tests catching bugs before production deployment.
View ProjectAgentic AI System with LangGraph
June 1, 2026 – Present
Designed and deployed multi-node AI agent system with conditional routing and autonomous decision-making capabilities. Implemented type-safe state management system using TypedDict for context-aware responses across multi-turn conversations. Built intelligent query analysis pipeline routing user requests through optimal processing workflows improving response accuracy.
View ProjectLaptop Price Predictor
June 1, 2026 – Present
Developed an end-to-end Machine Learning web application to predict laptop prices based on technical specifications (RAM, storage, processor, brand) using Python and Flask. Performed data collection, preprocessing, and feature engineering to prepare high-quality input data. Trained and optimized a Random Forest Regressor model, achieving an R² score of 85%. Integrated the trained model into a functional web interface for real-time price prediction.
View ProjectCustomer Churn Prediction MLOps System
Unknown
June 1, 2026 – Present
Agentic AI System with LangGraph
Unknown
June 1, 2026 – Present
Laptop Price Predictor
Unknown
June 1, 2026 – Present
Government Hospital Web Application
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and hands-on experience in the MLOps domain, aligning well with the target role. The diversity of projects, from predictive models to agentic AI systems and MLOps infrastructure, shows a broad technical curiosity and willingness to tackle different challenges. The focus on practical applications and deployment suggests a results-oriented approach. The candidate is currently pursuing a Bachelor's degree, indicating a continuous learning mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a focus on practical, deployable solutions. The emphasis on reducing deployment time and achieving high reproducibility suggests an operational mindset. Collaboration skills are mentioned in the internship description, indicating an ability to work in cross-functional teams. However, without direct interview data, a deeper assessment of soft skills is limited.