Senior ML Engineer - Healthcare LLMs & AI Systems

Venkat Reddy Meka

Email: *****************@*****.*** Mobile: 314-***-****

PROFESSIONAL SUMMARY

5+ years of experience as a Machine Learning Engineer specializing in developing and optimizing large language models (LLMs) and deep learning systems for healthcare applications. Proven ability to enhance AI systems through rigorous evaluation and testing.

Extensive experience in healthcare data, with a strong understanding of clinical and claims knowledge, ensuring the development of AI solutions that meet industry standards and requirements.

Proficient in Python and experienced with frameworks such as PyTorch and TensorFlow, enabling the implementation of cutting-edge machine learning models and workflows.

Hands-on experience with LLMs including GPT and similar architectures, focusing on real-world applications and production environments to drive impactful results.

Skilled in designing and executing automated evaluation and regression testing pipelines, enhancing the reliability and performance of AI systems.

Strong communication skills, adept at creating clear technical documentation, prompts, and training materials to guide model behavior and improve user interaction.

Demonstrated ability to work independently in ambiguous environments, taking ownership of projects and driving improvements in AI model performance and efficiency.

SKILLS

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

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

Natural Language Processing: NLP, Text Classification, Sentiment Analysis, Named Entity Recognition, Tokenization, Language Modeling, Text Generation, Chatbot Development

Deep Learning: Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Generative Adversarial Networks, Autoencoders, LSTM, Attention Mechanisms

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

Data Handling: Data Preprocessing, Data Cleaning, Data Augmentation, Feature Engineering, Data Visualization, ETL Processes, SQL Databases, NoSQL Databases

Model Evaluation: A/B Testing, Cross-Validation, Performance Metrics, Confusion Matrix, ROC Curve, Precision-Recall, F1 Score, Model Benchmarking

Healthcare Knowledge: Clinical Data Analysis, Claims Processing, Healthcare Regulations, Patient Data Management, Medical Terminology, Health Informatics, Electronic Health Records, HIPAA Compliance

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

Project Management: Agile Methodologies, Scrum, Kanban, Project Planning, Risk Management, Resource Allocation, Documentation, Workflow Optimization

WORK EXPERIENCE

Mastercard - O'Fallon, MO

Senior Machine Learning Engineer - Dec 2024 to Present

Spearheaded the development and optimization of Large Language Models (LLMs) using Python and PyTorch, enhancing model performance by 30% through advanced fine-tuning techniques.

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

Collaborated with cross-functional engineering and product teams to deploy AI features, utilizing AWS for cost-efficient model training and inference, resulting in a 25% reduction in operational costs.

Developed high-quality training datasets and evaluation workflows, employing automated testing methodologies to validate AI outputs and enhance model reliability.

Analyzed model performance metrics, identifying failure patterns and implementing corrective measures to address accuracy gaps and hallucinations, leading to a 15% improvement in output consistency.

Created comprehensive technical documentation and evaluation artifacts, demonstrating strong communication skills and facilitating knowledge transfer across teams.

Leveraged HuggingFace tools to streamline model-training workflows, significantly reducing training time by 40% while maintaining high-quality output standards.

Conducted regression testing and benchmarking for AI systems, ensuring robust evaluation of model performance and reliability in production environments.

Mentored junior engineers on best practices in machine learning and AI system design, fostering a culture of continuous learning and innovation within the team.

Championed the ongoing maintenance and enhancement of existing AI systems, contributing to long-term strategic goals and operational excellence.

Technologies Used: Python, PyTorch, AWS, LLMs, HuggingFace, TensorFlow, NLP, AI systems, automated testing, data evaluation

Boeing - St. Louis, MO

Applied AI Engineer - Sep 2023 to Nov 2024

Engineered and optimized deep learning models for AI applications, focusing on healthcare data, which improved predictive accuracy by 20% through innovative model architectures.

Developed and maintained training pipelines and datasets, utilizing TensorFlow and Python to streamline the model training process and enhance data quality.

Collaborated with product teams to design and implement automated evaluation pipelines, ensuring high standards of output quality and reliability for AI systems.

Analyzed and documented model performance, identifying edge cases and implementing strategies to mitigate hallucination behavior in AI outputs.

Executed functional and automated tests to validate AI system behavior, ensuring compliance with industry standards and enhancing user trust in AI solutions.

Engaged in cross-team collaboration to review partner and vendor work, providing insights and recommendations for continuous improvement of AI features.

Created detailed prompts and instructions for model training, showcasing strong communication skills and contributing to the overall success of AI projects.

Participated in the design and execution of benchmarking pipelines, evaluating AI system performance against established metrics and driving improvements.

Contributed to the development of AI evidence engines, focusing on LLMs and their applications in healthcare, resulting in innovative solutions for real-world challenges.

Provided mentorship to junior team members, fostering a collaborative environment and promoting knowledge sharing within the AI engineering team.

Technologies Used: Python, TensorFlow, deep learning, AI systems, healthcare data, automated evaluation, model training, LLMs, data pipelines, benchmarking

T-Mobile - Hyderabad, India

Data Scientist - Jan 2022 to Jun 2023

Developed and implemented machine learning models to analyze customer behavior, utilizing Python and TensorFlow, which increased customer retention rates by 15%.

Collaborated with cross-functional teams to design data-driven solutions, enhancing operational efficiency and driving strategic initiatives within the organization.

Conducted exploratory data analysis (EDA) and feature engineering, improving model accuracy by identifying key variables that influence customer decisions.

Automated data processing workflows, leveraging cloud technologies to reduce processing time by 30% and improve data accessibility for analytics teams.

Created visualizations and reports to communicate insights effectively to stakeholders, demonstrating strong analytical and presentation skills.

Participated in the development of predictive models for churn analysis, utilizing advanced statistical techniques to inform business strategies and initiatives.

Engaged in continuous learning and development, staying updated on industry trends and advancements in machine learning and data science methodologies.

Collaborated with engineering teams to integrate machine learning models into production systems, ensuring scalability and reliability of data-driven solutions.

Mentored interns and junior data scientists, providing guidance on best practices in data analysis and machine learning model development.

Contributed to the enhancement of existing data science processes, identifying areas for improvement and implementing innovative solutions.

Technologies Used: Python, TensorFlow, machine learning, data analysis, cloud technologies, predictive modeling, data visualization, EDA, feature engineering, automation

PayPal - Hyderabad, India

Data Scientist - Jun 2020 to Dec 2021

Designed and developed machine learning algorithms to detect fraudulent transactions, utilizing Python and advanced statistical techniques, which reduced fraud rates by 20%.

Collaborated with product and engineering teams to implement data-driven solutions, enhancing user experience and driving revenue growth through targeted initiatives.

Conducted data mining and analysis to uncover trends and insights, informing strategic decisions and improving overall business performance.

Developed and maintained dashboards and reporting tools to visualize key performance indicators (KPIs), facilitating data-driven decision-making across departments.

Automated data collection and preprocessing workflows, leveraging cloud platforms to enhance data availability and processing efficiency.

Engaged in A/B testing and experimentation to evaluate the effectiveness of new features and enhancements, providing actionable insights for product development.

Presented findings and recommendations to stakeholders, showcasing strong communication skills and the ability to translate complex data into actionable strategies.

Participated in cross-functional teams to drive innovation and improve data science practices within the organization, fostering a culture of collaboration and knowledge sharing.

Mentored junior analysts on best practices in data analysis and machine learning, contributing to team development and skill enhancement.

Contributed to the continuous improvement of existing algorithms and models, ensuring alignment with industry standards and best practices.

Technologies Used: Python, machine learning, data analysis, cloud platforms, A/B testing, data visualization, fraud detection, automation, statistical techniques, reporting

CERTIFICATIONS

AWS Certified Machine Learning - Specialty

Azure AI Engineer Associate (AI-102)

TensorFlow Developer Certificate

Google Professional Data Engineer

EDUCATION

Masters in Information Systems Saint Louis University 3.8 GPA

Bachelors in Computer Science GITAM University 85%

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