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Hi, I’m Dedeepya Palakurthi, an AI/ML Engineer with 3+ years of experience building scalable machine learning and AI solutions across healthcare and enterprise domains. I specialize in predictive modeling with XGBoost and Scikit-learn, deep learning with TensorFlow and PyTorch, and end-to-end ML pipelines using PySpark, Pandas, SQL, and Airflow. I’ve deployed production models with Docker and cloud platforms (AWS SageMaker, Azure ML, Vertex AI) and have hands-on experience with Generative AI and LLMs, RAG systems, and real-time APIs via FastAPI. I’m also focused on Responsible AI through SHAP explainability, bias detection, and privacy-compliant governance. I enjoy turning experimentation into business impact, collaborating with cross-functional teams, and building reliable, scalable solutions that move from prototype to production. I’m passionate about maintaining governance, monitoring, and thoughtful AI practices that align with healthcare and enterprise needs.…Hi, I’m Dedeepya Palakurthi, an AI/ML Engineer with 3+ years of experience building scalable machine learning and AI solutions across healthcare and enterprise domains. I specialize in predictive modeling with XGBoost and Scikit-learn, deep learning with TensorFlow and PyTorch, and end-to-end ML pipelines using PySpark, Pandas, SQL, and Airflow. I’ve deployed production models with Docker and cloud platforms (AWS SageMaker, Azure ML, Vertex AI) and have hands-on experience with Generative AI and LLMs, RAG systems, and real-time APIs via FastAPI. I’m also focused on Responsible AI through SHAP explainability, bias detection, and privacy-compliant governance. I enjoy turning experimentation into business impact, collaborating with cross-functional teams, and building reliable, scalable solutions that move from prototype to production. I’m passionate about maintaining governance, monitoring, and thoughtful AI practices that align with healthcare and enterprise needs.

Dedeepya Palakurthi

Full Stack Developer, Back-End Developer, Web Developer, +5

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Hi, I’m Dedeepya Palakurthi, an AI/ML Engineer with 3+ years of experience building scalable machine learning and AI solutions across healthcare and enterprise domains. I specialize in predictive modeling with XGBoost and Scikit-learn, deep learning with TensorFlow and PyTorch, and end-to-end ML pipelines using PySpark, Pandas, SQL, and Airflow. I’ve deployed production models with Docker and cloud platforms (AWS SageMaker, Azure ML, Vertex AI) and have hands-on experience with Generative AI and LLMs, RAG systems, and real-time APIs via FastAPI. I’m also focused on Responsible AI through SHAP explainability, bias detection, and privacy-compliant governance. I enjoy turning experimentation into business impact, collaborating with cross-functional teams, and building reliable, scalable solutions that move from prototype to production. I’m passionate about maintaining governance, monitoring, and thoughtful AI practices that align with healthcare and enterprise needs.…Hi, I’m Dedeepya Palakurthi, an AI/ML Engineer with 3+ years of experience building scalable machine learning and AI solutions across healthcare and enterprise domains. I specialize in predictive modeling with XGBoost and Scikit-learn, deep learning with TensorFlow and PyTorch, and end-to-end ML pipelines using PySpark, Pandas, SQL, and Airflow. I’ve deployed production models with Docker and cloud platforms (AWS SageMaker, Azure ML, Vertex AI) and have hands-on experience with Generative AI and LLMs, RAG systems, and real-time APIs via FastAPI. I’m also focused on Responsible AI through SHAP explainability, bias detection, and privacy-compliant governance. I enjoy turning experimentation into business impact, collaborating with cross-functional teams, and building reliable, scalable solutions that move from prototype to production. I’m passionate about maintaining governance, monitoring, and thoughtful AI practices that align with healthcare and enterprise needs.

Available to hire

Hi, I’m Dedeepya Palakurthi, an AI/ML Engineer with 3+ years of experience building scalable machine learning and AI solutions across healthcare and enterprise domains. I specialize in predictive modeling with XGBoost and Scikit-learn, deep learning with TensorFlow and PyTorch, and end-to-end ML pipelines using PySpark, Pandas, SQL, and Airflow. I’ve deployed production models with Docker and cloud platforms (AWS SageMaker, Azure ML, Vertex AI) and have hands-on experience with Generative AI and LLMs, RAG systems, and real-time APIs via FastAPI. I’m also focused on Responsible AI through SHAP explainability, bias detection, and privacy-compliant governance.

I enjoy turning experimentation into business impact, collaborating with cross-functional teams, and building reliable, scalable solutions that move from prototype to production. I’m passionate about maintaining governance, monitoring, and thoughtful AI practices that align with healthcare and enterprise needs.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Software Engineer at CVS Health

July 1, 2025 - Present

Contributed to the Cloud Infrastructure Modernization project, migrating monolithic systems to microservices using Docker, Kubernetes, and AWS Lambda, enabling scalable deployments across pharmacy operations. Developed backend services in Go and Node.js, integrating with MongoDB and RESTful APIs to improve system response time by 35%. Implemented CI/CD pipelines using GitHub Actions, automating build, test, and deployment workflows, reducing manual release efforts by 60%. Monitored service health and performance using Prometheus, Grafana, and the ELK Stack to proactively resolve infrastructure bottlenecks.

Software Engineer (Intern) at CVS Health

January 1, 2025 - May 1, 2025

Contributed to the Smart Form Autofill System, leveraging NLP techniques to extract patient information from unstructured documents and auto-populate digital healthcare forms. Built scalable Python modules using Hugging Face Transformers and TensorFlow for entity recognition and classification, enhancing data extraction accuracy to over 90%. Integrated the solution with backend services through GraphQL APIs and secured data transmission using OAuth2 to comply with HIPAA standards. Automated form validation workflows using Selenium, improving test coverage and reducing QA time by 40%. Collaborated within a Kanban-driven team and applied the Strategy Pattern for flexible model deployment.

Software Engineer at Infosys BPM

May 1, 2021 - July 1, 2023

Spearheaded the design and implementation of the Customer Service Chatbot Integration, embedding a real-time support assistant within the client’s CRM using Angular, Spring Boot, and WebSocket. Built RESTful APIs to facilitate real-time communication between the chatbot interface and backend services. Enforced JWT-based authentication, ensuring secure interactions. Crafted interactive UI components with Angular, HTML5, and CSS3, contributing to a 25% increase in support team efficiency. Streamlined backend data access by writing optimized SQL queries and managing data in MySQL, reducing data retrieval time by 30%. Orchestrated build and deployment automation with Jenkins and GitLab CI, and validated reliability through JUnit and TestNG. Engaged in Agile ceremonies to align deliverables with evolving business goals.

AI/ML Engineer at CVS Health

July 1, 2025 - Present

Spearheaded the creation of a predictive modeling solution utilizing XGBoost and Scikit-learn to forecast medication non-adherence and optimize pharmacy-led engagement strategies. Engineered scalable ETL and feature engineering pipelines with PySpark, Pandas, and Airflow to process large-scale pharmacy and claims datasets, reducing data preparation time by 35%. Deployed containerized ML services using Docker and Azure ML to deliver real-time risk scoring APIs, improving model response performance by 22% across integrated healthcare applications. Implemented semantic search capabilities using Pinecone and advanced embedding models to enhance internal knowledge retrieval for pharmacists and clinical stakeholders. Strengthened model governance by integrating SHAP-based explainability, bias validation frameworks, and continuous monitoring dashboards to ensure compliance with healthcare data privacy and responsible AI standards.

AI/ML Engineer (Intern) at CVS Health

January 1, 2025 - May 1, 2025

Built classification models using TensorFlow and Scikit-learn to detect anomalous pharmacy claims, reducing false positives by 21% and supporting fraud investigation teams. Analyzed large-scale healthcare datasets using SQL and Pandas, applying feature selection and statistical validation techniques to improve model precision and strengthen risk scoring logic. Developed RESTful inference endpoints with FastAPI and implemented experiment tracking through MLflow, reducing deployment turnaround time by 30% within a staging environment. Conducted bias evaluation, performance benchmarking, and structured documentation to align predictive models with healthcare compliance standards and responsible AI practices.

AI/ML Engineer at Infosys BPM

May 1, 2021 - July 1, 2023

Developed NLP and Transformer-based models to automate extraction of structured data from high-volume financial and insurance documents, improving processing efficiency and accuracy. Designed and optimized supervised learning models using XGBoost and Scikit-learn for customer churn prediction, improving retention campaign targeting accuracy by 24%. Engineered scalable data preprocessing and feature engineering workflows with Pandas and NumPy, reducing manual data cleansing effort by 40% across multiple client engagements. Orchestrated batch and near real-time ETL pipelines using Apache Airflow and advanced SQL to streamline model-ready dataset generation within enterprise data warehouses. Deployed containerized machine learning solutions using Docker and AWS SageMaker, achieving 99% service availability in production environments. Applied clustering and segmentation techniques to uncover behavioral insights, enabling data-driven personalization strategies for global clients. Implemente