Rishitha Katamareddy

Gen AI Engineer at Optum

Designed and implemented agentic AI architectures enabling autonomous planning, reasoning, decision-making, and execution using Large Language Models (LLMs). Built single-agent and multi-agent systems with role-based agents, task delegation, collaboration, and consensus-driven workflows. Implemented advanced reasoning patterns (ReAct, Chain-of-Thought, Tree-of-Thoughts, Planner–Executor, Self-Reflection loops). Integrated tool-calling and function execution with APIs, databases, web search, code execution, and enterprise services. Architected agent memory systems using embeddings and vector databases. Built end-to-end Retrieval-Augmented Generation (RAG) pipelines with document loaders, chunking, embeddings, re-ranking, and grounding. Applied prompt engineering techniques including system prompts, dynamic prompts, prompt chaining, and autonomous prompt optimization. Fine-tuned and adapted LLMs using PEFT, LoRA, and instruction tuning, with structured evaluation and feedback loops. Or

Gen AI ML Engineer at Walgreens

Designed, developed, and deployed end-to-end Machine Learning and Generative AI solutions across text, image, and multimodal use cases. Built and optimized Generative AI applications using large language models (LLMs), diffusion models, and embedding models. Fine-tuned speech recognition models with GPU-accelerated training; implemented prompt engineering, system prompts, and safety guardrails to improve accuracy, controllability, and reliability. Integrated vector databases and RAG pipelines to enable context-aware AI; deployed models with Docker/Kubernetes on cloud infrastructure via CI/CD. Implemented model evaluation, drift and bias monitoring, and ensured AI safety and compliance. Collaborated with cross-functional teams to translate business problems into ML/GenAI solutions, delivering measurable impact.

Deep Learning Engineer at Accenture India

Designed and developed deep learning models for computer vision, NLP, and structured data problems using TensorFlow and PyTorch. Implemented CNNs, RNNs, LSTMs, GRUs, and transformer-based architectures with transfer learning from pretrained models. Built image/video inference pipelines for object detection, segmentation, and temporal pattern analysis. Performed hyperparameter tuning and model optimization; deployed REST APIs for inference and containerized pipelines. Collaborated with data scientists and engineers to translate business requirements into DL solutions and maintained reproducible experimentation.

ML Engineer at Cognizant | India

Designed, developed, and deployed end-to-end ML solutions including data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. Implemented supervised and unsupervised learning, deep learning models (CNN, RNN, Transformer), NLP pipelines, and time-series analysis. Applied MLOps practices such as model versioning, CI/CD, automated retraining, and monitoring. Ensured model explainability, fairness, and robustness via SHAP/LIME, drift and bias detection, and continuous evaluation.

Deep Learning Engineer at Accenture | India

Designed and developed deep learning models using TensorFlow and PyTorch for computer vision, NLP, and structured data problems. Implemented neural architectures including CNNs, RNNs, LSTMs, and GRUs with transfer learning from pretrained models (ResNet, VGG, Inception, BERT). Developed image and video inference pipelines for object detection, segmentation, and temporal pattern analysis. Performed hyperparameter tuning and model optimization using grid search, random search, regularization, and dropout. Evaluated models using standard performance metrics and cross-validation. Worked with large-scale datasets and GPUs. Implemented model deployment pipelines for inference via REST APIs and containerized environments. Collaborated with data scientists and engineers to translate business requirements into DL solutions. Conducted model debugging and error analysis, maintained experiment tracking and documentation. Stayed up to date with deep learning research and best practices to incorpora

ML Engineer at Cognizant

Designed, developed, and deployed end-to-end Machine Learning solutions, covering data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. Implemented supervised and unsupervised learning algorithms including linear/logistic regression, decision trees, random forests, gradient boosting, SVMs, k-means, and DBSCAN. Built and optimized Deep Learning models using TensorFlow, Keras, and PyTorch for real-world applications. Developed Neural Network architectures such as ANN, CNN, RNN, LSTM, GRU, and Transformers for vision, NLP, and time-series data. Applied computer vision techniques including image classification, object detection, image segmentation, and transfer learning with pretrained models. Implemented NLP pipelines for text classification, sentiment analysis, named entity recognition, and language modeling. Performed model optimization and tuning with hyperparameter optimization, regularization, dropout, batch normalization, and learning rate s