Anand Mani Tripathi

Engineered robust end-to-end ETL pipelines and monitoring systems using Python, RabbitMQ, and Redis to ingest, transform, and store millions of market events daily, ensuring accurate and timely real-time risk analytics under heavy load.

Refactored a monolithic trade-feed system into modular, class-based Python microservices, enhancing maintainability and accelerating feature rollout and instrument onboarding for diverse asset classes including Commodities, Equities, FX, Rates, and Bonds worldwide.

Automated comprehensive GitLab CI/CD workflows, Dockerizing services and orchestrating multi-stage test suites, reducing release cycles by over 80% (from 5 minutes to under 1 minute) and maintaining zero-downtime deployments.

Profiled and optimized backend systems and infrastructure, identifying critical bottlenecks and reducing service restart latency by 85% (from 3 minutes to 25 seconds), significantly improving reliability during peak market volatility.

Maintained a Python/AWS SaaS risk-computation platform, implementing autoscaling policies, real-time health checks, and IAM-based secure access controls integrated with Bitbucket and GitLab.

Integrated RESTful APIs and RabbitMQ message queues to support continuous live data ingestion, reconciliation, and downstream analytics across multiple asset classes in a distributed environment.

Mentored junior engineers through structured code reviews and pair-programming sessions, and provided production support to Quantitative Researchers, Traders, and Portfolio Managers, swiftly resolving critical live-trade issues.

Conducted an extensive literature review of 10+ NLP and ML research papers across syntax, semantics, and transformer architectures, synthesizing insights for three novel editor-intelligence hypotheses.

Generated and curated synthetic datasets via back-translation, morphological inflection, and manual annotation using Pandas scripts, enriching training diversity by 10x for robust error modeling.

Designed and implemented end-to-end Python/TensorFlow pipelines with custom data loaders, training loops, and model exporters to train and deploy BERT/BART variants into the Google Docs prototype.

Developed precision, recall, and F1-score evaluation modules with similarity-filter thresholds in NumPy and SciPy, achieving a 12% F1 uplift and integrating automated metric logging into the production CI pipeline.

Architected and built a Flask-based backend for an interactive career-assessment portal, integrating psychologist-designed scoring algorithms and dynamic user dashboards with MySQL for personalized recommendations.

Implemented multi-layer caching strategies and optimized database queries to halve response times and reduce memory usage to under 10% of capacity, even under peak user traffic.

Authored Python scripts to scrape, clean, and normalize career data from government and educational websites, creating a comprehensive structured repository for backend decision logic and reporting.

Deployed and managed the full-stack application on Google Cloud Platform, configuring load balancers, IAM roles, and automated backup/restore procedures to guarantee 99.9% uptime and enterprise-grade security compliance.