Tejas Navalkhe

I have 3 years of experience in AI/ML and software engineering. I work on contract-based projects for clients in the UK and internationally, delivering business solutions designed to enhance operational efficiency and drive revenue growth.

I am an AI/ML Engineer with 3+ years of diverse experience across fintech, transportation, and edtech, specialising in building scalable, production-grade machine learning systems. At Co Wheels Car Club (UK), I developed real-time dynamic pricing models and demand forecasting tools, boosting projected revenue by 19.32%. My role combined machine learning, API development, and business analytics to deliver actionable insights for strategic decision-making.

Previously, at Finvestox Capital, I led the development of low-latency algorithmic trading platforms with sub-millisecond execution, real-time prediction systems, and scalable cloud deployments using FastAPI, Redis, Docker, and AWS. I’ve also built NLP-based recommendation engines, automated pipelines, and CI/CD systems as a freelance engineer, serving over 15 clients globally.

I hold an MSc in Data Science with a specialisation in AI (Distinction) from Newcastle University and am passionate about end-to-end AI solutions—from LLM-powered apps and recommendation systems to dynamic pricing and real-time analytics. My core strengths include NLP (BERT, GPT), MLOps, cloud-native development, and clean, efficient software design.

I hold an MSc in Data Science with a Specialisation in Artificial Intelligence from Newcastle University (Distinction). My academic focus included machine learning, deep learning, big data engineering, and AI system development. For my dissertation, I developed a dynamic pricing simulation tool for Co Wheels Car Club (UK), leveraging ML models, demand factors, and fine-tuning strategies to optimise pricing based on location, time, and vehicle type, directly contributing to the company’s transition to dynamic pricing.

Before that, I earned a B.Tech in Computer Science Engineering from PIEMR, India, with a CGPA of 8.78. My undergraduate thesis involved building a face recognition-based attendance system with 99.38% accuracy, and I contributed to university-level projects, including a high-traffic event management website. My curriculum covered core areas like data structures, operating systems, DBMS, machine learning, and cloud computing.