Pavan Bharadwaj A
Ai Ml Engineer @PayPal
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WORK HISTORY
Ai Ml Engineer @PayPal
US
Designed RAG pipelines for finance and compliance workflows, improving retrieval accuracy by 38% and reducing manual review cycles across 2024.• Developed fraud-risk ML models with optimized feature pipelines, lowering false positives by 22% and improving precision in high-volume transaction streams.• Built multi-agent automation for KYC/AML investigations, cutting analyst review time by 50% over two quarters through structured task orchestration. • Implemented high-throughput inference services with FastAPI, async processing, and vector caching, reducing API latency from 1.2s to under 500ms.• Built GPU-optimized training and ONNX inference achieving 2× speedup for fraud-rule evaluation.• Created drift and anomaly dashboards using Prometheus/Grafana, reducing model incidents by 25%.• Containerized AI microservices with Kubernetes and automated CI/CD pipelines, accelerating deployment cycles from monthly to weekly. • Implemented RBAC, encrypted data flows, and audit logging to meet PayPal’s 2024 compliance requirements. • Delivered LLM-powered case summarization boosting dispute-resolution speed 30%.
EDUCATION
Auburn University at Montgomery
Master of Science - MS, Computer Science
Bapatla Engineering College
Bachelor of Technology - BTech, Information Technology
ABOUT PAVAN BHARADWAJ A
AI/ML Engineer | Open to New OpportunitiesI bridge the gap between AI potential and production reality. I am an AI/ML Systems Engineer with 4+ years of experience building real, production-grade intelligence into enterprise products. I specialize in the intersection of LLM Engineering, Backend Architecture, and Cloud Infrastructure—turning complex ideas into stable, scalable AI services. Core Areas of Expertise• LLM Engineering: Designing advanced RAG pipelines, prompt engineering, and evaluation frameworks using LangChain and Azure OpenAI.• Systems Architecture: Building high-performance backends with Python (FastAPI) and.NET Core, utilizing event-driven designs with Kafka.• Cloud & MLOps: Deploying and scaling applications via Kubernetes (K8s), Docker, and Terraform across Azure and AWS.• Reliability & Observability: Hardening production environments with Prometheus, Grafana, and ELK to ensure AI systems are predictable and governed. My Philosophy: Production-First AIIn the world of LLMs,\"it works on my machine\" isn\'t enough. I focus on:• Efficiency: Using caching and async execution to slash latency and improve throughput.• Trust: Improving embedding quality and retrieval flows for high-accuracy, reliable RAG.• Scalability: Implementing robust LLMOps and CI/CD for seamless, automated deployments. Technical Toolkit• AI/ML: LangChain, RAG, Fine-tuning, Vector DBs, MLflow, Azure OpenAI• Infrastructure: Azure ML, AWS, Kubernetes, Docker, Terraform• Backend: Python (FastAPI).NET Core, Kafka, SQL, Event-driven Architecture• Monitoring: Prometheus, Grafana, ELK Stack Open to WorkI am currently seeking roles where I can shape high-performance AI platforms and LLM-driven applications. I am particularly interested in:• AI/ML Systems Engineer• LLM Engineer / AI Architect• MLOps / Platform EngineerIf you’re looking for someone to build AI systems that actually make a difference—faster decisions, clearer insights, and reliable automation—let’s connect!
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