● Built scalable machine learning pipelines using PySpark and Airflow for real-time clickstream data, improving recommendation accuracy by 22%.

● Designed and deployed low-latency inference systems using PyTorch, TorchServe, and Kubernetes, achieving 2x faster API response times and reducing infrastructure costs.

● Developed advanced recommendation systems leveraging RAG, vector search (FAISS), and LLMs, increasing upsell conversion rates by 15%.

● Implemented multi-agent AI workflows using LangGraph and LlamaIndex to enhance contextual understanding and response relevance in LLM-powered applications.

● Engineered real-time streaming pipelines with Kafka and Spark Streaming, improving anomaly detection coverage by 60% and enabling proactive user engagement insights.

● Optimized distributed model training using PyTorch Lightning and mixed precision techniques, improving training performance by 40%.

● Automated end-to-end ML workflows using MLflow and AWS SageMaker, reducing deployment downtime by 70% and ensuring continuous model freshness.

● Migrated and scaled ML infrastructure on AWS (EKS, Terraform), reducing deployment time from hours to under 20 minutes through robust CI/CD pipelines.

● Unified 50TB+ of EHR and claims data by developing PySpark ETL pipelines that cut data preparation time by 35%, giving the team faster access to reliable clinical datasets.

● Improved the quality of patient insights by fine-tuning a BERT-based medical NLP model on 500K+ unstructured EHR notes, increasing the availability of meaningful features by 40%.

● Built a disease-risk prediction model in TensorFlow that delivered an AUC of 0.91, helping healthcare teams identify chronic-condition risks earlier and with greater accuracy.

● Increased transparency and clinician trust by integrating SHAP explainability and ensuring HIPAA-aligned audit trails, which encouraged a 20% boost in AI-driven decision usage.

● Designed an efficient MLOps workflow using Azure DevOps and MLflow that improved model versioning and reduced deployment cycles to just 48 hours.

● Deployed ML models as Docker microservices managed through Kubernetes, using FHIR-compatible APIs to maintain sub-200ms inference latency for 10,000+ healthcare professionals.

● Ran bias analysis and mitigation across demographic groups, reducing prediction gaps by 15% and aligning the models with Responsible AI guidelines.

● Cut cloud compute costs by 70% by applying model quantization and leveraging TensorRT for faster, hardware-optimized inference.

● Built a RASA-based triage assistant that responds to 20,000+ patient queries per day, helping care teams focus on urgent and high-complexity cases.

● Created secure, HIPAA-compliant Power BI dashboards that turned model outputs into clear public-health insights, guiding a $5M annual intervention strategy.

● Developed end-to-end NLP and LLM solutions to process 8M+ SWIFT payment messages using BERT and RAG-based pipelines, improving classification accuracy from 72% to 91% and reducing manual investigation effort.

● Built and deployed real-time fraud detection and anomaly detection systems using Kafka, PySpark, and SageMaker, reducing fraud latency from 2.3s to 0.8s and lowering false positives by 40%.

● Delivered significant financial impact by improving loan approval accuracy by 28% and reducing default risk by $4.1M through scalable credit risk and prediction models.

● Designed production-grade ML pipelines and inference systems using AWS SageMaker, MLflow, Docker, and ECS, enabling reliable processing of 120K+ transactions per day with low-latency predictions.

● Enhanced operational efficiency by implementing LLM-powered assistants and RAG-based summarization systems, reducing analyst triaging time by 35% and improving compliance and decision-making workflows.