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.
Education
Certifications
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