Virendrasinh Chavda

I am a Data Scientist and Machine Learning Engineer with over 2 years of experience delivering end-to-end AI and ML solutions across industries such as manufacturing, cybersecurity, and technology. My expertise spans core machine learning, Generative AI (GenAI), and advanced data engineering, enabling me to tackle complex problems and create impactful business solutions.

🔑 Core Skills:

• Programming & Tools: Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, Spark, Docker, Kubernetes, LangChain, MLflow.
• Cloud Platforms: AWS, GCP, Azure.
• Machine Learning: Predictive modeling, anomaly detection, time-series forecasting, statistical analysis, MLOps.
• Generative AI: Retrieval-Augmented Generation (RAG), GPT-4, AI-powered chatbots, NLP, Gemini model integration.
• Data Engineering: ETL pipelines, large-scale data processing, feature engineering, automation.

🌟 Key Achievements:

• Developed predictive maintenance systems with time-series forecasting, reducing downtime by 20%.
• Built anomaly detection models to optimize processes, saving $5,000 monthly.
• Implemented Generative AI-powered chatbots and virtual assistants, improving customer engagement by 20%.
• Created RAG pipelines and integrated LangChain for intelligent search and retrieval systems.
• Built scalable AI text generation tools leveraging GPT-4, deployed on Google Kubernetes Engine (GKE).

I thrive at the intersection of ML and GenAI, combining technical expertise with creative problem-solving to deliver measurable results. I’m passionate about using AI to uncover actionable insights, optimize decision-making, and enhance user experiences.

📍 Location: UK-based, open to on-site, hybrid and remote roles.

⏳ Availability: Immediately available for new opportunities.

ML/AI Engineer (Part-time) Dec 2024 – Present

Anima People | London, UK

• Develop and deploy machine learning models for anomaly detection, fraud prevention, and threat intelligence.
• Design and optimize risk scoring algorithms for individual user profiles.
• Build and maintain scalable ETL pipelines to preprocess and analyze large volumes of real-time security data from diverse sources, utilizing tools like Apache Kafka, Spark, and AWS.
• Collaborate with cross-functional teams to integrate ML solutions into cybersecurity platforms.
• Implement explainable AI (XAI) frameworks using SHAP and LIME, ensuring transparency in model outputs to support regulatory compliance and stakeholder confidence.

ESRC Business and Local Government Data Research Centre | Colchester, UK

• Conducted in-depth analysis of financial data, identifying key trends across 120+ UK HE institutions.
• Collaborated with stakeholders to deliver actionable insights, enhancing decision-making. 
• Developed and deployed an interactive dashboard with dynamic comment section using Streamlit and APIs.
• Implemented OAuth 2.0 and encryption to secure sensitive financial data.

Hitech Digital Solutions LLP | Ahmedabad, India

• Developed a predictive maintenance system for 50+ manufacturing machines, achieving 86% accuracy in failure prediction and reducing downtime by 20% through advanced time-series sensor analysis.
• Reduced quality control (QC) time by 40% and saved $5,000 per month by implementing an error detection system for manufacturing drawings, achieving 89% classification accuracy using YOLOv8.
• Developed and deployed scalable APIs using FastAPI, containerized with Docker, and orchestrated on cloud platforms to ensure efficient and reliable data processing. 
• Built and optimized ETL pipelines to clean, aggregate, and preprocess time-series sensor data.
• Leveraged Spark for data transformations on large datasets, reducing processing time by 30%.
• Designed and conducted A/B tests to validate the performance of AI-driven error detection systems against traditional manual QC processes, analyzing metrics such as accuracy, processing time, and cost savings.
• Applied Bayesian optimization with Gaussian Processes to optimize model hyperparameters.

Smart Technologies Outsourcing Services Pvt. Ltd.| Jaipur, India

• Designed, fine-tuned, and deployed advanced Generative AI models, leveraging architectures like GPT, DALL-E, and Stable Diffusion for diverse business applications.
• Engineered Retrieval-Augmented Generation (RAG) pipelines, integrating retrieval systems with generative models to enhance contextual accuracy and user experience.
• Created advanced prompts for text and image generation, optimizing outputs for domain-specific use cases using LangChain and similar frameworks.
• Integrated OpenAI and Hugging Face APIs for real-time AI-driven functionalities across applications, ensuring seamless interaction with external data sources.
• Collaborated with cross-functional teams to ensure seamless integration of Generative AI tools into production environments, boosting workflow automation and customer engagement by 20%.
• Delivered presentations to stakeholders, showcasing Generative AI capabilities and their business impact, fostering trust and driving adoption of AI solutions.

MSc Data Science (Distinction) | University of Essex, Colchester (UK) Oct 2023 – Oct 2024