Mohith Reddy S
Professional Summary
AI Engineer with 3+ years of experience progressing from retail data science to healthcare ML engineering and production AI. Designs and ships reliable, monitored AI services that bridge research and product by defining measurable release criteria for accuracy, latency, reliability and cost.
Technical Skills
Work Experience
- Designed and deployed cloud inference services for enterprise customers, achieving 99.9% availability through autoscaling, dependency isolation, and graceful fallback strategies.
- Cut partner deployment cycles from 8 weeks to 3 weeks by creating reusable Python SDKs, infrastructure templates and standardized evaluation workflows for integrations.
- Implemented end-to-end automated evaluation of user journeys (retrieval, tool execution, context retention) and increased task-completion accuracy to 89% by surfacing regression and groundedness metrics pre-release.
- Reduced manual QA effort by 60% by developing automated test suites that reproduced 30+ production-reported behaviors, enabling research to prioritize model-level fixes over one-off complaints.
- Introduced runtime guardrails and multi-path fallbacks that prevented unsupported answers and tool failures, lowering production incidents during model timeouts and dependency failures.
- Defined shared release measures for accuracy, latency, reliability and cost with product and research teams, enabling consistent adoption across seven enterprise environments within 90 days.
- Built a clinical RAG system that reduced document review time by 42% while keeping clinicians involved for high-risk decisions, improving clinician throughput and safety.
- Enabled semantic search across 5+ million medical records with 94% retrieval precision by integrating vector search and domain-specific embeddings for clinical query patterns.
- Developed clinical NLP models for diagnoses, medications and ICD-10 extraction, achieving a 91% F1-score and enabling downstream analytics and decision support.
- Deployed production services processing 200,000+ daily transactions with p99 latency under 300ms through optimized inference pipelines and batching strategies.
- Reduced false-positive clinical alerts by 33% via threshold calibration and operational feedback loops with care-management teams, increasing signal-to-noise for clinicians.
- Integrated AI outputs with Epic via HL7 FHIR APIs and added PII masking, access controls, audit trails and monitoring to meet compliance and enterprise governance requirements.
- Improved demand-planning accuracy by 18% across 500+ stores using XGBoost models and historical sales, promotions and inventory features, reducing stockouts and overstock.
- Built Spark ETL pipelines processing 15+ TB weekly of transaction and clickstream data, enabling near-real-time analytical refreshes for planning teams.
- Reduced analytical turnaround from four days to same-day by creating reusable BigQuery data models and transformation templates for downstream reporting.
- Segmented 25M+ loyalty members and operationalized segments for targeted campaigns that delivered a 12% higher conversion rate versus baseline.
- Designed and delivered Tableau dashboards used by 80+ stakeholders across six business units to drive demand planning and promotional analysis.
- Automated reporting and data-validation checks that saved 20+ analyst hours per week and surfaced store-level forecast degradation and hidden stockout issues.
Projects
- Implemented 6+ evaluation metrics, including Recall@K, Precision@K, Hit Rate, MRR, BLEU, and ROUGE, to evaluate retrieval and response quality.
- Built benchmark and regression-testing workflows to compare different LLM and retrieval configurations.
- Added semantic-similarity evaluation and automated reporting to make model performance easier to track across experiments.
- Developed A/B comparison and statistical-testing workflows to support data-driven model selection.
- Structured repeatable test cases around retrieval accuracy, response quality, and end-to-end application behavior.
- Processed approximately 25 GB of human movement video for posture and movement analysis.
- Extracted 33 pose landmarks using MediaPipe and engineered features based on joint angles, torso alignment, symmetry, and movement.
- Evaluated 3 machine-learning models, including Random Forest, XGBoost, and SVM, using 5-fold cross-validation.
- Achieved approximately 77% accuracy and 0.76 macro F1 using the Random Forest model.
- Extended the system toward real-time posture analysis by combining computer vision, AI inference, and interactive application components
- Built an automated pipeline covering data ingestion, preprocessing, geospatial analysis, feature engineering, and crop-health assessment.
- Used NDVI analysis to measure vegetation health and identify changes in crop conditions over time.
- Integrated multiple external data sources and APIs for satellite, weather, and agricultural information.
- Developed a Streamlit application to visualize crop-health trends, field-level results, weather conditions, and monitoring indicators.
- Added fallback data workflows to keep the application functional during external API or data-source limitations.
- Developed an interactive financial intelligence dashboard covering 50 U.S. states, enabling users to search, filter, and compare state-level consumer interest-rate information through a centralized interface.
- Built a state-level interactive U.S. map using D3.js and TopoJSON, connecting geographic selections with the underlying rate table to improve financial data exploration.
- Consolidated 4+ key financial indicators, including Fed Funds Rate, Treasury yields, mortgage rates, and prime rate, into a single market-monitoring dashboard.
- Designed a conversational finance Q&A assistant supporting questions around interest rates, credit cards, financial institutions, and investment concepts.
- Implemented dynamic table rendering, state filtering, search functionality, interactive visualizations, and API integration hooks using JavaScript ES6+and designed an API-ready architecture that separates presentation, data logic, and external-data integration, enabling future connection to live financial data sources with minimal application changes.
Education
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