Rahul Dhímån

● Built, trained, and optimized machine learning models including Generative AI and LLM
fine‐tuning using AWS SageMaker.
● Developed deep learning NLP models for text classification, summarization, and entity recognition.
● Implemented AWS DevOps and containerized ML workloads using Docker for scalable
deployments.
● Created end‐to‐end ML pipelines with Amazon SageMaker including training, deployment, and
automation.
● Monitored deployed ML models using Amazon CloudWatch to track drift and operational metrics.
● Utilized AWS services such as S3, Lambda, and Step Functions in scalable cloud architectures.
● Trained large-scale time-series forecasting models and built automated retraining pipelines.

• Developed AI-powered applications
• Built and optimized ML models
• Time series analysis, Time series predictive models
• Integrated AI models with Fastapi, Django & Streamlit, making solutions scalable & user-friendly.

• Spearheading machine learning projects and developing AI-powered applications, leveraging expertise in predictive analysis
and root cause analysis using Causal inference.
• Expertise in various aspects of data science, including data cleaning, churn prediction, recommendation systems, fine-tuning
models, exploratory data analytics, churn prediction, hypothesis testing and feature engineering.
• Data Collection and Analysis:
◦ Gather and interpret data from various sources, including financial databases, APIs, and internal systems.
◦ Clean, validate, and transform raw data into a format suitable for analysis.
• Financial Modelling:
◦ Develop and maintain financial models to forecast trends and identify potential risks and opportunities.
◦ Perform scenario analysis and sensitivity testing to support strategic planning and decision-making.
• Performance Reporting:
◦ Prepare regular reports and dashboards to monitor key performance indicators (KPIs) and financial metrics.
◦ Collaborate with cross-functional teams to understand business requirements and develop tailored reports.
• Data Visualization:
◦ Create compelling visualisation, such as charts, graphs, and interactive dashboards, using tools like Tableau, Power
BI, or Python libraries.
◦ Communicate complex data insights effectively to stakeholders through visual presentations.
• Ad Hoc Analysis:
◦ Conduct ad hoc analysis to support business inquiries, product development, and marketing initiatives.
◦ Generate actionable insights and recommendations based on data findings.
• Data Quality and Integrity:
◦ Monitor data quality, identify discrepancies, and implement solutions to maintain data integrity.
◦ Establish and enforce best practices for data management and documentation.

Contextual learning-based algorithm for patent literature, Data Scraping with real-time google search, Mongo DB or MySQL, Data
processing, machine learning projects with transformers.

I got acquainted with the various sectors and processes of the industry with insight into safety aspects to be followed in the
structured environment of the industry. Key skills that will add values:
Documentation increase tech transfer by 99.9% | Technical Research