Machine Learning Engineer - LLMs & Healthcare AI

Mahesh Sandeep Dandamudi

Email: **************@*****.*** Mobile: 937-***-****

PROFESSIONAL SUMMARY

7+ years of experience as a Machine Learning Engineer specializing in developing and optimizing large language models (LLMs) and deep learning systems within healthcare applications.

Proven expertise in designing and implementing instruction orchestration and evaluation workflows for LLM-based systems, ensuring high-quality AI outputs and system behavior.

Strong background in healthcare data, with hands-on experience in machine learning, natural language processing (NLP), and deep learning, contributing to AI evidence engines.

Skilled in writing high-quality prompts, instructions, and training examples to shape model behavior, enhancing the performance of AI systems.

Experienced in building and maintaining training pipelines, datasets, and evaluation workflows, driving improvements in model performance and reliability.

Proficient in Python, with extensive experience using PyTorch and TensorFlow for model training and optimization, including cost-efficient inference deployments on AWS.

Excellent communication skills, adept at creating technical documentation and evaluation artifacts, ensuring clarity and understanding across engineering and product teams.

SKILLS

Programming Languages: Python, Java, R, SQL, C++, JavaScript, Scala, Bash

Machine Learning Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn, HuggingFace, MXNet, FastAI, OpenCV

Natural Language Processing: NLP, Text Preprocessing, Sentiment Analysis, Named Entity Recognition, Tokenization, Language Modeling, Text Generation, Word Embeddings

Deep Learning: Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, LLMs, Model Optimization, Transfer Learning, Hyperparameter Tuning

Cloud Platforms: AWS, Azure, Google Cloud Platform, EC2, S3, Lambda, SageMaker, Azure ML

Data Engineering: Data Pipelines, ETL Processes, Data Warehousing, SQL Databases, NoSQL Databases, Data Cleaning, Data Transformation, Big Data Technologies

Healthcare Knowledge: Clinical Data Analysis, Claims Processing, Healthcare Regulations, Patient Data Management, Medical Terminology, Health Informatics, Epidemiology, Health Data Standards

Model Evaluation: Performance Metrics, A/B Testing, Regression Testing, Benchmarking, Model Validation, Error Analysis, Accuracy Measurement, Confusion Matrix

Soft Skills: Communication, Problem-Solving, Team Collaboration, Adaptability, Critical Thinking, Time Management, Stakeholder Engagement, Leadership

Project Management: Agile Methodologies, Scrum, Kanban, Project Planning, Risk Management, Resource Allocation, Documentation, Progress Tracking

WORK EXPERIENCE

Charles Schwab Corporation - Westlake, Texas, USA

Generative AI Engineer - Mar 2025 to Present

Developed and optimized Large Language Models (LLMs) using Python and PyTorch, enhancing model performance by 30% through fine-tuning and advanced training techniques.

Designed and implemented instruction orchestration workflows for LLM-based systems, ensuring seamless integration with existing AI features and improving operational efficiency.

Collaborated with cross-functional engineering and product teams to deploy AI features, leveraging AWS for cost-efficient inference deployments and GPU utilization.

Analyzed model performance, identifying accuracy gaps and hallucination patterns, leading to a 25% increase in reliability through iterative improvements.

Created high-quality prompts and training examples to shape model behavior, resulting in enhanced user interaction and satisfaction metrics.

Established automated evaluation and regression testing pipelines, utilizing HuggingFace tools to ensure consistent AI output quality and reliability.

Contributed to the ongoing maintenance and enhancement of existing AI systems, driving continuous improvement in performance and user experience.

Led knowledge-sharing sessions to educate team members on LLM optimization techniques and best practices, fostering a culture of innovation and collaboration.

Documented technical processes and evaluation artifacts, ensuring clarity and accessibility for future reference and team onboarding.

Managed project timelines and deliverables with minimal direction, demonstrating strong ownership in ambiguous problem spaces.

Technologies Used: Python, PyTorch, AWS, HuggingFace, LLMs, TensorFlow, GPU, AI, NLP, deep learning, automated testing, regression testing, evaluation workflows, instruction orchestration, model optimization

GM Financial - Fort Worth, Texas, USA

Machine Learning Engineer - Aug 2024 to Feb 2025

Engineered and fine-tuned machine learning models for predictive analytics, utilizing Python and TensorFlow to enhance decision-making processes within the organization.

Developed and maintained training pipelines and datasets, ensuring data integrity and optimizing model training efficiency to support business objectives.

Collaborated with engineering teams to deploy AI solutions, focusing on model performance evaluation and reliability, resulting in a 20% reduction in processing time.

Conducted comprehensive analyses of model outputs, identifying failure patterns and implementing corrective measures to improve accuracy and consistency.

Designed functional and automated tests to validate AI outputs, ensuring adherence to quality standards and regulatory compliance in financial services.

Assisted in the integration of NLP techniques to enhance customer interaction models, improving engagement metrics by 15%.

Participated in code reviews and knowledge-sharing sessions, promoting best practices in machine learning and fostering a collaborative team environment.

Documented technical specifications and evaluation processes, contributing to the team's knowledge base and facilitating onboarding for new members.

Engaged in continuous learning and professional development, staying updated on industry trends and advancements in machine learning technologies.

Supported project management efforts by tracking deliverables and timelines, ensuring alignment with organizational goals and stakeholder expectations.

Technologies Used: Python, TensorFlow, machine learning, NLP, automated testing, data pipelines, predictive analytics, model evaluation, financial services, AI

GM Financial - Fort Worth, Texas, USA

Machine Learning Engineer Intern - Nov 2023 to Aug 2024

Assisted in the development and optimization of machine learning models, utilizing Python and deep learning frameworks to support ongoing projects.

Collaborated with senior engineers to build and maintain training datasets, ensuring data quality and relevance for model training and evaluation.

Participated in the design and execution of automated tests to validate AI outputs, contributing to the overall quality assurance process.

Analyzed model performance metrics, identifying areas for improvement and supporting the implementation of enhancements to boost accuracy.

Engaged in team meetings to discuss project progress and share insights on machine learning methodologies, fostering a collaborative learning environment.

Documented processes and findings, contributing to the team's knowledge repository and facilitating knowledge transfer among team members.

Supported the integration of NLP techniques into existing models, enhancing their capabilities and improving user interaction.

Gained hands-on experience with model training workflows and evaluation processes, developing a solid foundation in machine learning practices.

Assisted in the preparation of technical documentation and reports, ensuring clarity and accessibility for stakeholders.

Contributed to team efforts in maintaining compliance with industry standards and best practices in machine learning.

Technologies Used: Python, deep learning, machine learning, NLP, automated testing, data management, model evaluation, AI

Tata AIA Life Insurance - Mumbai, India

Data Scientist - Jun 2018 to Jul 2023

Developed and implemented predictive models using Python and machine learning techniques, improving customer retention rates by 20% through targeted strategies.

Analyzed large datasets to extract actionable insights, leveraging statistical analysis and data visualization tools to support business decision-making.

Collaborated with cross-functional teams to design and execute data-driven marketing campaigns, enhancing customer engagement and conversion rates.

Conducted A/B testing and performance evaluations of marketing strategies, optimizing campaign effectiveness and increasing ROI by 15%.

Created and maintained dashboards for real-time monitoring of key performance indicators, enabling proactive decision-making across departments.

Developed training materials and conducted workshops to enhance team members' understanding of data science methodologies and tools.

Engaged in continuous improvement initiatives, identifying opportunities to streamline processes and enhance data quality across the organization.

Documented analytical processes and findings, ensuring transparency and accessibility for stakeholders and team members.

Participated in industry conferences and workshops to stay updated on emerging trends and technologies in data science and analytics.

Mentored junior data scientists, providing guidance and support in their professional development and project work.

Technologies Used: Python, machine learning, data analysis, statistical analysis, data visualization, A/B testing, dashboards, predictive modeling, analytics, customer engagement

CERTIFICATIONS

AWS Certified Machine Learning - Specialty

Microsoft Azure AI Engineer Associate (AI-102)

EDUCATION

Masters in Computer Science University of Texas at Arlington 3.9 GPA

Bachelors in Computer Science Engineering SRKR Engineering College 8.18 CGPA

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