Feb 2025 – Present · 1 yr 4 mos · Basel, Switzerland · On-site

• As a Research and Teaching Assistant under Prof. Dr. Lukas Burkart, I am actively involved in exploring the integration of AI-driven solutions within the Department of History.
• Leveraging advanced Large Language Models (LLMs) to digitize and structure scholarly texts of ancient historians, improving online accessibility and usability.
• Applying artificial intelligence and machine learning techniques to historical materials, transforming scholarly portraits into CGI animations to enhance historical comprehension and visualization.
• Collaborating with RISE community to develop AI-powered methodologies for data annotation, visualization, and statistical analysis in historical research.

Nov 2024 – Feb 2025 · 3 mos · Basel, Switzerland · On-site

• Collaborated on the SNSF-funded Travail-Travel-Traders project, led by Dr. Lesley Nicole Braun, to analyze African women’s trading practices and transnational connections between Africa and China.
• Developing scalable data pipelines to extract and process multimedia content from social media platforms using BeautifulSoup, Selenium, and asyncio. Implementing NLP models with Hugging Face Transformers and spaCy for sentiment analysis and topic modeling of social media content.
• Designing multimodal machine learning models using TensorFlow and PyTorch to analyze text, video, and metadata for cross-platform trade insights.

Nov 2023 – Sep 2024 · 10 mos · Bengaluru, Karnataka, India

• Developed and deployed Amazon Connect contact flows, leveraging AWS Lambda functions to automate customer service workflows, resulting in a 30% reduction in response times and a 25% increase in customer satisfaction ratings.
• Orchestrated comprehensive testing and validation of the Amazon Connect setup, ensuring 99% uptime for voice services, which directly contributed to improved patient trust and service reliability.
• Enhanced reporting capabilities by integrating Amazon Connect with AWS analytics tools, providing actionable insights into call patterns and patient needs, leading to a 25% increase in proactive patient outreach initiatives.
• Utilized AWS Kinesis to ingest and process real-time customer interaction data from Amazon Connect, enabling advanced analytics and driving data-driven decision-making.
• Monitored system performance and optimized call flow configurations, resulting in a 15% reduction in dropped calls and improving overall service quality for patients.
• Implemented data governance and security best practices, leveraging AWS IAM, AWS KMS, and AWS Config to ensure data privacy, compliance, and reliability, achieving an 85% uptime for critical data pipelines.

Feb 2022 – Nov 2023 · 1 yr 9 mos · Bengaluru, Karnataka, India

• Spearheaded an integration project that bridged the gap between Salesforce and end users, leveraging AWS to achieve a 35% reduction in response time, enabling real-time interactions and significantly enhancing user satisfaction.
• Designed and implemented scalable APIs and background workers using Python, AWS Lambda, API Gateway, Kafka, RDS, and S3, effectively managing thousands of license requests daily, which increased processing efficiency by 40%.
• Integrated CloudWatch and SNS with Lambda to establish comprehensive alerts and proactive monitoring mechanisms, reducing downtime incidents by 85%, thereby ensuring seamless operations and minimizing potential revenue loss.
• Optimized data exchange processes by implementing security protocols that protected sensitive information, which not only complied with industry regulations but also reduced the risk of data breaches, ultimately saving the company significant costs related to compliance and reputational damage.

May 2021 – Dec 2021 · 7 mos · Chennai, Tamil Nadu, India

• Collaborated with cross-functional engineering teams to comprehend requirements and translate them into effective software solutions, aligning with real-time data analytics goals.
• Implemented advanced analytics features in Alteryx, which enhanced the accuracy of predictive models by 30%. This improvement in data quality led to more informed business decisions, ultimately optimizing budget allocations and increasing ROI on data-driven initiatives.
• Leveraged Apache Spark's in-memory processing capabilities to reduce data processing time by 50%, enabling real-time analytics that supported faster decision-making and improved operational efficiency.
• Optimized ETL workflows by integrating Kafka, which increased data processing throughput by 50%. This improvement minimized the time spent on data extraction and transformation, allowing the team to focus on higher-value tasks and reducing project timelines by 30%.
• Implemented automated data quality checks using Python scripts, ensuring the integrity and accuracy of real-time data, and drove the identification and resolution of data anomalies by 60%.
• Developed and maintained robust data pipelines, enabling the seamless flow of data from various sources to the data warehouse, resulting in a 30% increase in data processing speed and a 20% reduction in data latency.
• Collaborated with data analysts and business stakeholders to define and implement data models that supported complex analytical queries and facilitated data-driven decision-making.
• Documented data pipelines, created user guides, and conducted training sessions to enable self-service data access and empower data consumers across the organization.

May 2021 – Jul 2021 · 2 mos · Chennai, Tamil Nadu, India

Feb 2021 – Jul 2021 · 5 mos · Chennai, Tamil Nadu, India