Computational Mathematics in AI for Drug Discovery
- https://www.linkedin.com/pulse/two-proteins-one-conversation-how-numbers-predict-binding-babu-rss6c/
AWS Cloud Contributions
Senior IT & AI/ML Engineering Leader with 20+ years of experience driving enterprise architecture, software modernization, and AI/ML platform delivery. Proven track record of leading cross-functional engineering organizations to deliver scalable, secure, and compliant cloud-native applications in highly regulated industries (Life Sciences , Banking and Telecom). Expertise in shaping product roadmaps, managing multi-million-dollar budgets, establishing LLMOps governance, and developing engineering managers to accelerate the adoption of generative AI and predictive machine learning models that deliver measurable business outcomes
LinkedIn https://www.linkedin.com/in/dharanibabu2025
Models @ HuggingFace https://huggingface.co/aifinitydigital
Computational Mathematics in AI for Drug Discovery
https://www.linkedin.com/pulse/two-proteins-one-conversation-how-numbers-predict-binding-babu-rss6c/
Cloud Contributions
PROJECTS
EpiDock AI: An antibody - antigen Interaction & Structural Prediction Platform
DocGen — AI-Assisted Document Generation for GxP Manufacturing ( for CRDMO - Sai Life Sciences)
DocGen , developed for a leading CRDMO, is a GMP compliance initiative . It turns the slowest step in GxP operations — authoring and revising controlled documents — into an AI-assisted workflow with human QA approval at every release. It generates new SOPs and technical packages grounded in the site's own documents and the regulatory corpus (ICH Q7/Q9/Q10/Q12, FDA guidance, EU GMP, PIC/S), and executes controlled modifications to existing documents with full traceability.
RetroSynthesis AI : Optimal synthesis Pathway Platforms for Pharma Molecules ( for CRDMO - Sai Life Sciences )
GxP Doc Retrieval System : AI Powered Document Intelligence System ( for Takeda Pharmaceuticals )
Architected a Product Safety Incident Classification (PSIC) platform to automatically assess severity of customer complaints across Smoke Detectors, CO Detectors, and Fire Extinguishers, reducing manual legal review effort.
Designed the end-to-end data pipeline: scheduled jobs extracted daily customer incidents from Salesforce into Snowflake, and AWS Glue ETL jobs loaded the curated data into SQL Server on AWS to feed the ML classification model
Used Glue crawlers and the Glue Data Catalog to maintain table metadata and schemas, making pipeline data discoverable across teams.Built data quality checks for incomplete incident records.
Maintained product reference data and mapped incoming incidents to the master product list, so classification stayed consistent across product lines.
Preserved Salesforce case IDs( lineage) end to end, giving traceability from each source record to its model classification.
Partnered with legal and business leadership to define the product roadmap. Governed the engineering budget by choosing on-premises LLM deployment over SaaS APIs, controlling inference costs while keeping data private.
STELLAW | AI-Powered Legal Research for Indian Jurisprudence
CapitalMind | LLM-Powered Capital Markets Research Engine
Architected CapitalMind, an AI-native investment intelligence platform enabling investment bankers, equity analysts, and capital markets teams to analyze institutional behavior using natural-language queries.
InHouse CoPilot Solution
Architected and deployed an in-house AI Copilot for enterprise .NET / C# codebases, enabling secure, context-aware developer assistance without reliance on external SaaS tools. Integrated Cline(RooCode) with .NET C# MCP Server
IPaaS Solutions ( ws02 )
ML | Systems Analytics Lead - Capacity Planning & Forecasting
Owned the resource allocation and capacity strategy for 8,500+ Linux servers. Utilized ML forecasting to drive enterprise-wide infrastructure budget optimization, clearly communicating financial risks and ROI to senior banking executives.
Ordinary Least Squares (OLS) linear regression, producing an interpretable relationship of the form y = mx + c for capacity extrapolation.