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Hi, I'm Lalitha Guru Swaminathan, a passionate developer and machine learning engineer with experience in building cloud-based NLP pipelines and AI workflows for enterprise clients. I enjoy collaborating with global teams and working in Agile environments to deliver secure, scalable, and innovative AI-powered solutions. I am enthusiastic about leveraging technologies such as Azure AI, AWS, and PyTorch to solve complex problems in natural language processing, computer vision, and spatial data analytics. With a strong background in software development, data science, and research, I've contributed to cutting-edge projects in document summarization, semantic search, brain tumor segmentation, genomic data analysis, and environmental prediction models. I'm committed to continuous learning and applying the latest MLOps and cloud deployment best practices to create impactful and efficient AI models that address real-world challenges.…Hi, I'm Lalitha Guru Swaminathan, a passionate developer and machine learning engineer with experience in building cloud-based NLP pipelines and AI workflows for enterprise clients. I enjoy collaborating with global teams and working in Agile environments to deliver secure, scalable, and innovative AI-powered solutions. I am enthusiastic about leveraging technologies such as Azure AI, AWS, and PyTorch to solve complex problems in natural language processing, computer vision, and spatial data analytics. With a strong background in software development, data science, and research, I've contributed to cutting-edge projects in document summarization, semantic search, brain tumor segmentation, genomic data analysis, and environmental prediction models. I'm committed to continuous learning and applying the latest MLOps and cloud deployment best practices to create impactful and efficient AI models that address real-world challenges.

Lalitha Guru Swaminathan

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Hi, I'm Lalitha Guru Swaminathan, a passionate developer and machine learning engineer with experience in building cloud-based NLP pipelines and AI workflows for enterprise clients. I enjoy collaborating with global teams and working in Agile environments to deliver secure, scalable, and innovative AI-powered solutions. I am enthusiastic about leveraging technologies such as Azure AI, AWS, and PyTorch to solve complex problems in natural language processing, computer vision, and spatial data analytics. With a strong background in software development, data science, and research, I've contributed to cutting-edge projects in document summarization, semantic search, brain tumor segmentation, genomic data analysis, and environmental prediction models. I'm committed to continuous learning and applying the latest MLOps and cloud deployment best practices to create impactful and efficient AI models that address real-world challenges.…Hi, I'm Lalitha Guru Swaminathan, a passionate developer and machine learning engineer with experience in building cloud-based NLP pipelines and AI workflows for enterprise clients. I enjoy collaborating with global teams and working in Agile environments to deliver secure, scalable, and innovative AI-powered solutions. I am enthusiastic about leveraging technologies such as Azure AI, AWS, and PyTorch to solve complex problems in natural language processing, computer vision, and spatial data analytics. With a strong background in software development, data science, and research, I've contributed to cutting-edge projects in document summarization, semantic search, brain tumor segmentation, genomic data analysis, and environmental prediction models. I'm committed to continuous learning and applying the latest MLOps and cloud deployment best practices to create impactful and efficient AI models that address real-world challenges.

Available to hire

Hi, I’m Lalitha Guru Swaminathan, a passionate developer and machine learning engineer with experience in building cloud-based NLP pipelines and AI workflows for enterprise clients. I enjoy collaborating with global teams and working in Agile environments to deliver secure, scalable, and innovative AI-powered solutions. I am enthusiastic about leveraging technologies such as Azure AI, AWS, and PyTorch to solve complex problems in natural language processing, computer vision, and spatial data analytics.

With a strong background in software development, data science, and research, I’ve contributed to cutting-edge projects in document summarization, semantic search, brain tumor segmentation, genomic data analysis, and environmental prediction models. I’m committed to continuous learning and applying the latest MLOps and cloud deployment best practices to create impactful and efficient AI models that address real-world challenges.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Work Experience

Developer Intern at Tata Consultancy Services (TCS)

June 1, 2025 - Present

Built cloud-based NLP pipelines using Azure AI, Github, and Copilot Studio for automated document summarization and semantic search. Developed and deployed AI workflows on AWS integrating large-scale data pipelines and ML models for enterprise clients. Applied RAG techniques with JSON NoSQL-style datasets in Azure AI and integrated LLM APIs into business workflows. Collaborated within Agile teams globally to ensure secure and scalable solutions meeting tight deadlines.

Machine Learning Engineer Intern at Sustainable Living Labs

July 1, 2023 - August 28, 2025

Designed and deployed an English–Malay LLM-based translation model improving accuracy by 18% via contextual tuning. Optimized PyTorch computer vision models for energy monitoring by addressing latency and reproducibility issues in cloud environments. Led AI workflow demonstrations, gathered client requirements, and supported workflow adoption. Gained familiarity with MLOps (MLflow) and collaborated with Agile teams across three countries, cutting validation time by 30%.

Research Intern at Nagasaki University

October 1, 2022 - August 28, 2025

Designed SEEA-UNet, a novel hybrid model enhancing brain tumor segmentation using refined spatial and channel attention mechanisms. Verified and preprocessed over 1,200 annotated MRI images resolving inconsistencies and reducing segmentation error by 21%. Optimized hybrid U-Net in PyTorch achieving 0.0112 validation loss within 10 epochs to accelerate convergence and improve precision. Published research on arXiv and presented findings simplifying complex model mechanics for diverse audiences.

Research Assistant at Centre for Healthcare Advancement Innovation and Research (CHAIR)

May 1, 2022 - August 28, 2025

Performed quantitative analysis and built predictive models estimating warfarin dosage from genomic data, improving accuracy by 24%. Collaborated with clinicians, geneticists, and data scientists to align machine learning dosage prediction models with biomedical knowledge and pharmacogenomic goals. Cleaned and prepared over 5,000 genomic records resolving 98% missing data and reducing outlier variance by 15%, enhancing model input quality. Published findings validating mutation impacts through 3D structural bioinformatics.