Swaraj Khaire
Ai Engineer Applied Ml Systems (Production) @Mccooley\'s
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WORK HISTORY
Ai Engineer Applied Ml Systems (Production) @Mccooley\'s
Liverpool, GB
Owned the design and delivery of production-grade AI/ML systems using real-world hospitality data (10k–50k+ records), deployed in live business contexts.• Built end-to-end ML pipelines (data ingestion → feature engineering → training → evaluation → deployment-ready workflows).• Developed predictive demand and trend models, achieving 15–25% improvement over baseline approaches.• Automated data preprocessing and analytics workflows, reducing manual operational reporting effort by ~30–40%.• Applied a systems-first engineering approach, prioritising reproducibility, scalability, and measurable business impact.
EDUCATION
University of Liverpool
Master's degree, Computer Science
Sinhgad College of Engineering
BE, Production Engineering
ABOUT SWARAJ KHAIRE
I build production-grade AI and machine learning systems that move from research to real-world impact.Currently, I work at the intersection of Applied AI, ML engineering, and software systems, translating complex models into scalable, business-ready solutions used in live environments. Alongside this, I’m completing an MSc in Computer Science at the University of Liverpool, with a strong focus on machine learning, deep learning, and large-scale systems.Previously, I’ve delivered AI solutions across enterprise security analytics, GenAI systems, and data-driven platforms, including roles with EY (Security Analytics) and eClerx (AI/ML Engineering)—where my work improved model accuracy, system performance, and operational reliability at scale.My work spans: • AI & ML Engineering (end-to-end model development → deployment) • Generative AI & LLM systems (RAG, evaluation, optimisation) • Applied ML in real business contexts (security, analytics, decision systems) • Production software engineering (Python, scalable pipelines, cloud)Current MSc dissertation: Secure Peer-to-Peer Digital Cheque System, combining cryptography, distributed systems, and applied software engineering.I’m particularly interested in roles where AI is shipped, owned, and scaled—not just experimented with.Core strengths • Turning research into production • Strong engineering fundamentals • Clear ownership mindset • Comfortable operating across AI + software boundaries UK | Open to AI Engineer / Machine Learning Engineer / Applied Scientist / Software Engineer roles.
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