Okual Yenugu
• Seasoned AI/ML Engineer with 8+ years of experience delivering machine learning solutions that address complex business and operational challenges across banking, healthcare, and insurance domains.
About
• Seasoned AI/ML Engineer with 8+ years of experience delivering machine learning solutions that address complex business and operational challenges across banking, healthcare, and insurance domains.
• Hands-on experience building RAG pipelines, LLM evaluation workflows, and agentic AI systems using LangChain, Hugging Face, GPT-4, BERT, and LLaMA to deliver reliable generative AI outputs in production environments.
• Demonstrated expertise in designing deep learning models using CNNs, RNNs, Transformers, and reinforcement learning for NLP, computer vision, and time-series use cases.
• Skilled in taking models from development to production using Docker, Kubernetes, MLflow, and CI/CD pipelines on AWS and Azure, with applied model explainability using SHAP to support transparency and regulatory requirements across financial and healthcare domains.
• Experienced in building end-to-end data pipelines with Apache Spark, Hadoop, and Snowflake to support large-scale analytics and feature processing.
• Skilled in developing real-time APIs and services to integrate machine learning models into enterprise platforms and operational systems.
• Proficient in deploying scalable AI systems on AWS and Azure, using Docker and Kubernetes to support reliable, production-grade workflows.
• Specialized in natural language processing tasks, including text normalization, NER, intent detection, sentiment analysis, and transformer-based language models such as BERT and GPT-4.
• Knowledgeable in applying explainability techniques and bias control methods to improve model transparency, trust, and regulatory alignment.
• Collaborative contributor within Agile teams, using Git, JIRA, and structured delivery practices to support iterative development and on-time releases.
- 9 years' experience
- Joined June 2026
Industry experience
- Financial Services
- Healthcare
- Professional Services
More experts in Austin, United States
Work experience· 4 roles
Built a retrieval-augmented generation (RAG) pipeline using LangChain and FAISS to ground GPT-4 in internal banking knowledge, improving domain accuracy. Designed retrieval chains and prompt templates for controlled context injection and consistent responses. Developed LLM evaluation pipelines with prompt regression testing and structured rubrics, reducing manual QA. Created pairwise human-review datasets to track generative AI quality over time. Implemented human-in-the-loop feedback loops feeding into prompt refinement. Deployed NLP pipelines (entity extraction, intent detection, sentiment) with Hugging Face Transformers and NLTK; trained transformer models for semantic search and text classification, improving retrieval accuracy. Built FastAPI inference services, containerized with Docker, orchestrated via Kubernetes, and monitored with MLflow. Leveraged AWS Lambda/Glue/Step Functions for data processing and Jenkins for CI/CD. Applied reinforcement learning to create adaptive operat
Developed ML models with TensorFlow and XGBoost for clinical outcome prediction and risk classification, improving accuracy by ~18% over rule-based baselines. Built end-to-end Python data pipelines (NumPy, Pandas) and performed feature engineering for supervised/unsupervised tasks. Trained CNNs, LSTMs, and Transformer models for medical imaging and sequence tasks. Integrated Azure Cognitive Services, Synapse Analytics, and Data Factory to support scalable ML/data processing. Implemented REST APIs with Flask for real-time predictions; used Optuna for hyperparameter optimization, achieving ~10% F1-score gains. Ensured HIPAA compliance with data de-identification, access controls, and audit logging. Applied YOLO-based object detection to unstructured data. Packaged services with Docker and managed CI/CD with Jenkins. Delivered interactive dashboards in Power BI; collaborated in Agile/Scrum with JIRA.
Processed large-scale datasets using Apache Spark and Hadoop; performed feature engineering for predictive models. Built classification models (Logistic Regression, SVM, Random Forest, Decision Tree) and performed clustering to identify customer segments. Led hypothesis testing, A/B testing, ANOVA, and Bayesian analyses to validate outcomes. Managed data in Snowflake and integrated external/internal APIs to enrich analytics. Maintained automated data workflows, visualized findings with Seaborn/Matplotlib, and supported reproducible work with Git/GitHub. Coordinated cross-functional efforts in an Agile environment.
Education
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