Rahul K

Working as a Generative AI and Machine Learning Engineer responsible for designing and deploying enterprise-scale AI solutions leveraging Large Language Models and advanced ML pipelines. My work focuses on building production-ready GenAI systems that integrate with enterprise data platforms and cloud infrastructure. I design and implement Retrieval-Augmented Generation (RAG) architectures using LangChain, vector databases, and hybrid search techniques to enable intelligent document retrieval and automated insights. I also develop agentic AI workflows that allow multiple LLM agents to collaborate on complex tasks such as incident analysis, system diagnostics, and automated remediation. Additionally, I build scalable ML pipelines on AWS using services such as SageMaker, Lambda, and Step Functions for model training, deployment, and monitoring. My role also involves fine-tuning domain-specific language models, integrating LLM APIs, optimizing inference performance, and building REST APIs for production AI services. Through these initiatives, I help deliver AI-driven automation, analytics, and decision support systems across enterprise platforms.

AI Engineer responsible for designing and deploying machine learning and NLP solutions that enhanced fraud detection, risk analytics, and customer intelligence across banking platforms. Developed predictive models for credit risk scoring and transaction anomaly detection, improving fraud identification accuracy by 30%+ and reducing false positives through advanced feature engineering and model optimization. Built end-to-end NLP pipelines and transformer-based models to process millions of financial documents, support tickets, and transaction records, enabling automated classification, sentiment analysis, and entity extraction. Fine-tuned BERT and RoBERTa models, improving document classification performance by 25%+. Designed real-time fraud detection systems and recommendation engines leveraging deep learning and anomaly detection algorithms, supporting high-volume banking transaction streams. Deployed scalable ML services on AWS using SageMaker, EC2, Lambda, and S3, enabling low-latency inference and improving model deployment efficiency by 40% through automated MLOps pipelines and experiment tracking with MLflow. Collaborated with cross-functional teams to deliver explainable AI solutions using SHAP and LIME, ensuring model transparency and compliance with financial regulatory standards.

Served as a Data Scientist responsible for developing predictive analytics solutions and data-driven insights to support decision-making across government programs and operational initiatives. I worked extensively with large structured and unstructured datasets to identify trends, build forecasting models, and improve policy planning. My responsibilities included designing machine learning models, building scalable data pipelines, and performing statistical analysis using Python, R, and SQL. I collaborated closely with data engineers, analysts, and policy stakeholders to translate business problems into analytical solutions. I also developed interactive dashboards and visualizations using Tableau and Power BI to communicate insights to executive leadership. In addition, I implemented A/B testing frameworks, feature engineering strategies, and model evaluation techniques to improve predictive accuracy and operational performance.

Worked as a Data Analyst supporting data-driven decision-making through advanced analytics, reporting, and data pipeline development. My role focused on analyzing customer behavior, optimizing data workflows, and building analytical solutions to improve operational efficiency. I developed Python-based data processing and analytics scripts using Pandas, NumPy, and statistical libraries to perform customer segmentation, predictive analysis, and data validation. I also built ETL pipelines and data warehousing solutions to integrate data from multiple enterprise systems. Additionally, I created interactive dashboards and analytical reports using Tableau and advanced Excel tools to provide insights into business performance, product trends, and customer behavior. My work helped stakeholders make informed decisions based on reliable data insights.

Started my career as a Tableau Developer and Data Analyst supporting healthcare analytics initiatives. My responsibilities included developing dashboards, generating analytical reports, and transforming complex healthcare datasets into meaningful business insights. I designed and implemented Tableau dashboards that integrated data from multiple healthcare sources such as claims, pharmacy, and membership systems. I also performed data analysis and validation using SQL, Python, and SAS to support analytical studies and reporting requirements. In addition, I worked closely with business stakeholders to gather reporting requirements, build interactive visualizations, and improve reporting automation. My work helped streamline reporting processes and enabled better insights into healthcare data and operational performance.