AI Engineer with 2+ years in Machine Learning & Data Engineering
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AI/ML Engineer and Data professional with 2.5+ years of industry experience and an MSc in Data & Computational Science at University College Dublin. Skilled in building end-to-end machine learning pipelines in Python, applying deep learning frameworks (PyTorch), and working with large-scale data systems. Hands-on experience with object detection models (YOLOv8), predictive modelling, and data-driven insight delivery. Actively developing expertise in Large Language Models (LLMs), NLP techniques, prompt engineering, and Retrieval-Augmented Generation (RAG). Strong foundation in data engineering, ETL pipelines, and Agile cross-functional delivery. Passionate about responsible AI, continuous learning, and translating complex AI outputs into actionable value for business stakeholders.
University College Dublin
MSc in Data & Computational Science · Machine Learning & AI Technologies, Scientific Programming, Core Python for ML, Optimisation in ML, Modern Regression, Data Visualisation, Uncertainty Quantification
September 1, 2024 – Present
JNTU
B.Tech in Computer Science & Information Technology · OOP with Java, Python, Database Management Systems, Data Structures, Oracle SQL, PL/SQL
August 1, 2018 – April 1, 2022
Virtusa Consultancy Services
Oracle Developer
June 1, 2022 – July 1, 2024
Dublin, Leinster, Ireland
YOLOv8 Wildlife Detection - End-to-End ML Pipeline
June 6, 2026 – Present
Designed and implemented a complete ML lifecycle pipeline: data acquisition from NTLNP camera trap dataset, preprocessing and normalisation, feature engineering, multi-class model training across 17 species categories, optimisation, and evaluation. Applied model benchmarking using evaluation metrics including confusion matrices, precision-recall curves, and IoU statistics directly informing iterative model improvement. Automated dataset validation and class-wise performance reporting using Python (Pandas, NumPy), significantly reducing manual preprocessing effort. Documented model performance trade-offs across species categories to support responsible AI evaluation practices.
Business Intelligence Dashboard - Operational Performance Reporting
June 6, 2026 – Present
Built an interactive Power BI self-service dashboard analysing 13+ interrelated datasets, enabling autonomous operational reporting for business stakeholders. Implemented time intelligence features, dynamic filters, and geographic map visuals to surface seasonal and performance variance trends. Modelled data using Power Query and DAX with a star schema structure, following BI governance and data management best practices.
LLM & RAG Exploration Project
June 6, 2026 – Present
Exploring LLM capabilities through hands-on experimentation with Hugging Face Transformers and open-source language models, building familiarity with prompt engineering and context design. Prototyping basic Retrieval-Augmented Generation (RAG) patterns using vector similarity search to ground LLM outputs in domain-specific knowledge bases. Developing understanding of LLM evaluation, hallucination mitigation, and responsible AI practices through structured self-directed learning.
Socio-Economic Indicator Analysis Toolkit
June 6, 2026 – Present
Designed modular R functions for data ingestion, exploratory analysis, and statistical summaries across socio-economic datasets for Brazil, India, and Ireland. Produced reproducible research outputs and cross-country time-series performance insights using custom ggplot2 visualisation utilities.
PL-300 - Microsoft Certified: Power BI Data Analyst Associate
Microsoft
June 1, 2026 – Present
Oracle Database PL/SQL Developer Certified Professional
Oracle
June 1, 2026 – Present
NPTEL Certified - Programming in Python
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from deep learning (YOLOv8) to LLM exploration and business intelligence, indicates a broad interest and adaptability. Their stated passion for responsible AI and continuous learning aligns with a positive cultural fit for an evolving AI team. The experience in Agile environments also suggests good team collaboration potential.
Soft Skills & Operational Fit
The candidate demonstrates strong verbal and written communication skills, crucial for explaining complex AI concepts. They are comfortable with ambiguity, adaptable, and proactive, which are valuable traits in fast-paced, innovation-driven environments. Their growth mindset and familiarity with AI-assisted development workflows suggest a good operational fit for a modern AI engineering team.