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AI and Machine Learning Engineer with 3+ years of production experience building machine learning models in healthcare and financial services. Skilled in Python, SQL, TensorFlow, PyTorch, and Scikit-learn, with hands-on work across deep learning, NLP, and Generative AI. Experienced in data processing pipelines, Docker, Kubernetes, GCP, MLOps, REST API design, and monitoring model performance (including drift/latency) to improve reliability, speed, and production outcomes. Currently pursuing an MSc in Artificial Intelligence with an industrial placement.…AI and Machine Learning Engineer with 3+ years of production experience building machine learning models in healthcare and financial services. Skilled in Python, SQL, TensorFlow, PyTorch, and Scikit-learn, with hands-on work across deep learning, NLP, and Generative AI. Experienced in data processing pipelines, Docker, Kubernetes, GCP, MLOps, REST API design, and monitoring model performance (including drift/latency) to improve reliability, speed, and production outcomes. Currently pursuing an MSc in Artificial Intelligence with an industrial placement.

Umesh Bhukya

AI Engineer, Data Scientist, Programmer, +3

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AI and Machine Learning Engineer with 3+ years of production experience building machine learning models in healthcare and financial services. Skilled in Python, SQL, TensorFlow, PyTorch, and Scikit-learn, with hands-on work across deep learning, NLP, and Generative AI. Experienced in data processing pipelines, Docker, Kubernetes, GCP, MLOps, REST API design, and monitoring model performance (including drift/latency) to improve reliability, speed, and production outcomes. Currently pursuing an MSc in Artificial Intelligence with an industrial placement.…AI and Machine Learning Engineer with 3+ years of production experience building machine learning models in healthcare and financial services. Skilled in Python, SQL, TensorFlow, PyTorch, and Scikit-learn, with hands-on work across deep learning, NLP, and Generative AI. Experienced in data processing pipelines, Docker, Kubernetes, GCP, MLOps, REST API design, and monitoring model performance (including drift/latency) to improve reliability, speed, and production outcomes. Currently pursuing an MSc in Artificial Intelligence with an industrial placement.

Available to hire

AI and Machine Learning Engineer with 3+ years of production experience building machine learning models in healthcare and financial services. Skilled in Python, SQL, TensorFlow, PyTorch, and Scikit-learn, with hands-on work across deep learning, NLP, and Generative AI.

Experienced in data processing pipelines, Docker, Kubernetes, GCP, MLOps, REST API design, and monitoring model performance (including drift/latency) to improve reliability, speed, and production outcomes. Currently pursuing an MSc in Artificial Intelligence with an industrial placement.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AI & Machine Learning Engineer at C R Technologies

June 1, 2025 - Present

Built reusable Python and PySpark data processing pipelines for healthcare text ingestion, preprocessing, and feature engineering to create model-ready datasets for NLP and GenAI. Improved dataset reliability using profiling, missing-value checks, and schema validation for audit readiness. Developed NLP classification pipelines using Scikit-learn, TensorFlow, PyTorch, and BERT and benchmarked performance using precision/recall/F1 and latency. Built a healthcare document Q&A prototype using LangChain and OpenAI API with embeddings and RAG, ensuring grounded responses from approved sources only. Enhanced generalisation on imbalanced datasets using class weighting, hyperparameter tuning, dropout, and early stopping with validation-curve and error analysis. Containerised ML components with Docker and Kubernetes, exposed predictions via REST APIs with validation and structured outputs, and deployed to GCP/AWS. Implemented Git-based CI/CD with experiment tracking, model versioning, drift det

AI & Machine Learning Engineer at Virtusa (Financial Services)

August 1, 2022 - November 1, 2024

Designed ETL and data processing pipelines (Python, SQL, Pandas, PySpark) for financial services, improving quality with missing-value, outlier, and schema checks. Developed supervised and unsupervised models (Logistic Regression, Decision Trees, Random Forests, XGBoost, SVM, K-Means, KNN) chosen based on explainability, performance, and runtime requirements. Evaluated models using cross-validation and metrics such as ROC AUC, precision, recall, and F1-score to support release decisions. Built NLP pipelines using TF-IDF, embeddings, and BERT for text classification and sentiment analysis, replacing rule-based keyword approaches. Piloted retrieval-based Q&A for financial and enterprise documents using vector embeddings and semantic search to surface relevant clauses and figures. Trained deep learning workflows with CNNs using TensorFlow/Keras/PyTorch (including batch normalisation and dropout) to optimise for production. Deployed trained models as scalable microservices on Google Cloud

AI & Machine Learning Engineer at Virtusa

August 1, 2022 - November 30, 2024

Designed ETL and data processing pipelines (Python, SQL, Pandas, PySpark) for financial services data, improving quality using missing-value, outlier, and schema checks. Developed supervised and unsupervised models (Logistic Regression, Decision Trees, Random Forests, XGBoost, SVM, KNN, K-Means) with selection based on explainability, performance, and runtime needs. Evaluated models using cross-validation and metrics such as ROC AUC, precision, recall, and F1-score to support release decisions. Built NLP pipelines (TF-IDF, embeddings, BERT) for text classification and sentiment analysis, replacing rule-based keyword approaches. Piloted a retrieval-based Q&A tool for financial and enterprise documents using vector embeddings and semantic search. Built and trained deep learning workflows (TensorFlow/Keras/PyTorch) including CNNs with batch normalization and dropout for production use. Deployed trained models as scalable microservices on GCP/AWS using Docker and Kubernetes to support high