Machine Learning & NLP Engineer | LLM Enthusiast
Bengaluru, India • AI/ML Diploma, University of Hyderabad
AI-driven reliability and operations
At Accenture I design and productionize ML systems that predict sensor failures. By tightening data validation, crafting high-signal features, and testing models from logistic regression through gradient boosting, I raised accuracy and trust in critical monitoring pipelines.
Earlier at Cerner I built a Python-based NLP solution that cut problem resolution time by 30%. The POC, PICS (Problem Identification through Contextual Search), is integrated into Cerner's analytics platform MyJarvis and is under patent evaluation.
I care about operational excellence: clear documentation, disciplined train/validation/test splits, continuous performance monitoring, and close collaboration with domain experts to keep models aligned to business outcomes.
What I use to ship reliable ML
Selected work from Accenture and Cerner
Built robust validation, feature engineering, and ensemble models to forecast sensor outages with higher accuracy.
AccenturePython NLP pipeline that identifies ticket trends, cutting TAT by 30%; integrated into Cerner's analytics stack.
Patent EvaluationAutomated health checks and incident playbooks, improving response times for critical systems.
OperationsExplorations in translating natural language into SQL to accelerate analytics for stakeholders.
ResearchSharing learnings with the community
Posts on model evaluation, data quality, and operationalizing ML systems.
Documenting lessons from taking an NLP POC into production and patent review.
Experiments and commentary on bridging human questions to structured data retrieval.
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