Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Hi, I'm Vedant Kamleshkumar Sukhadia, a Machine Learning Engineer with over three years of experience in building and deploying machine learning solutions. I specialize in real-time recommendation systems, time series forecasting, and natural language processing applications. I enjoy collaborating with cross-functional teams in Agile environments to translate business needs into impactful ML models. I am proficient in Python, SQL, and Java, and skilled in frameworks like TensorFlow, PyTorch, and Huggingface. I also have hands-on experience deploying models with Docker, Kubernetes, and AWS SageMaker to ensure scalable and efficient production workflows. Exploring new challenges where I can apply my knowledge and continue growing in the AI and ML domain is something I'm passionate about.…Hi, I'm Vedant Kamleshkumar Sukhadia, a Machine Learning Engineer with over three years of experience in building and deploying machine learning solutions. I specialize in real-time recommendation systems, time series forecasting, and natural language processing applications. I enjoy collaborating with cross-functional teams in Agile environments to translate business needs into impactful ML models. I am proficient in Python, SQL, and Java, and skilled in frameworks like TensorFlow, PyTorch, and Huggingface. I also have hands-on experience deploying models with Docker, Kubernetes, and AWS SageMaker to ensure scalable and efficient production workflows. Exploring new challenges where I can apply my knowledge and continue growing in the AI and ML domain is something I'm passionate about.

Vedant Kamleshkumar Sukhadia

AI Engineer, Data Scientist, Developer, +1





Hi, I'm Vedant Kamleshkumar Sukhadia, a Machine Learning Engineer with over three years of experience in building and deploying machine learning solutions. I specialize in real-time recommendation systems, time series forecasting, and natural language processing applications. I enjoy collaborating with cross-functional teams in Agile environments to translate business needs into impactful ML models. I am proficient in Python, SQL, and Java, and skilled in frameworks like TensorFlow, PyTorch, and Huggingface. I also have hands-on experience deploying models with Docker, Kubernetes, and AWS SageMaker to ensure scalable and efficient production workflows. Exploring new challenges where I can apply my knowledge and continue growing in the AI and ML domain is something I'm passionate about.…Hi, I'm Vedant Kamleshkumar Sukhadia, a Machine Learning Engineer with over three years of experience in building and deploying machine learning solutions. I specialize in real-time recommendation systems, time series forecasting, and natural language processing applications. I enjoy collaborating with cross-functional teams in Agile environments to translate business needs into impactful ML models. I am proficient in Python, SQL, and Java, and skilled in frameworks like TensorFlow, PyTorch, and Huggingface. I also have hands-on experience deploying models with Docker, Kubernetes, and AWS SageMaker to ensure scalable and efficient production workflows. Exploring new challenges where I can apply my knowledge and continue growing in the AI and ML domain is something I'm passionate about.

Available to hire

Hi, I’m Vedant Kamleshkumar Sukhadia, a Machine Learning Engineer with over three years of experience in building and deploying machine learning solutions. I specialize in real-time recommendation systems, time series forecasting, and natural language processing applications. I enjoy collaborating with cross-functional teams in Agile environments to translate business needs into impactful ML models.

I am proficient in Python, SQL, and Java, and skilled in frameworks like TensorFlow, PyTorch, and Huggingface. I also have hands-on experience deploying models with Docker, Kubernetes, and AWS SageMaker to ensure scalable and efficient production workflows. Exploring new challenges where I can apply my knowledge and continue growing in the AI and ML domain is something I’m passionate about.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Advanced

Work Experience

Machine Learning Engineer Intern at CVS Health

May 31, 2025 - July 19, 2025

Developed a real-time, personalized product recommendation engine using PyTorch and XGBoost, optimizing upsell opportunities based on customer prescription data and behavioural patterns. Engineered scalable data pipelines using Spark and Airflow, reducing data processing latency by 35%, enabling near real-time ingestion of transaction data from web and in-store sources. Integrated SHAP-based model explainability for clinical review and compliance, ensuring interpretability of recommendations in a regulated healthcare environment. Deployed end-to-end ML workflows using MLflow and AWS SageMaker within a containerized (Docker + Kubernetes) infrastructure. Collaborated with data scientists, DevOps, and marketing teams in an Agile environment, contributing to weekly sprints and model iteration cycles. Achieved an 18% increase in click-through rate and contributed to a 12% lift in OTC product conversions during the A/B testing phase across pilot stores.

Machine Learning Engineer at Infosys

July 31, 2023 - July 19, 2025

Designed and deployed demand forecasting models using RNNs and traditional regression techniques to predict product sales across 30+ markets with seasonal and promotional variation. Processed and transformed historical sales, inventory, and external variables (e.g., weather, holidays) using Pandas and NumPy for feature engineering. Developed and trained models in TensorFlow and Scikit-learn, optimizing hyperparameters through grid search to improve forecasting accuracy. Integrated model predictions into the client's ERP system via REST APIs for daily demand planning and replenishment cycles. Built custom data ingestion pipelines from Hive tables and legacy flat files, ensuring clean and consistent data availability. Containerized model training environment using Docker for consistent development-to-production transition. Conducted A/B testing with business stakeholders, which led to a 15% improvement in forecast accuracy and a 12% reduction in stockouts across pilot regions. Automated