Rajnish Pandey

Domain: Crypto Trading | Risk | KYC | Compliance

• Led architecture and development of high-throughput, low-latency distributed systems supporting real-time risk evaluation for global crypto trading users
• Designed event-driven microservices processing high-volume transaction streams using Kafka and scalable backend services
• Built and optimized real-time data pipelines for KYC, fraud detection, and compliance workflows
• Improved system performance through latency optimization, efficient resource utilization, and concurrency handling
• Integrated external APIs and internal systems to enable seamless data flow between trading operations and compliance platforms
• Established engineering best practices for code quality, CI/CD, and scalable architecture design
• Mentored engineers and led hiring initiatives, fostering a culture of technical excellence and ownership

• Built AI-powered KYC validation systems enabling automated decision-making with explainability, significantly reducing manual review effort.
• Developed OCR + AI pipelines for real-time document processing and onboarding automation, improving user experience and KYC success rates.
• Designed fraud detection and compliance workflows, enhancing system reliability and reducing false positives in risk evaluation.

• Led 0→1 design and productionization of a sovereign AI Provider Routing platform, building a unified API layer to orchestrate multiple LLM providers.
• Developed intelligent routing & Auto-Router logic to dynamically optimize model selection based on cost, latency, throughput, and output quality.
• Implemented high-availability architecture with fallback and failover strategies, ensuring reliable, low-latency AI request handling at scale.
• Deployed LLM workloads on bare-metal with Intel TEE and dStack, enabling secure, privacy-preserving processing with strong data isolation guarantees.

Domain: Investment Banking | Securities Lending | Risk

• Engineered data-intensive platforms using Spark, Scala, and distributed storage systems (HDFS, HBase, Kudu)
• Built and optimized high-volume ETL pipelines and batch processing systems for financial data
• Developed high-performance APIs for integration with upstream/downstream trading and reporting systems
• Improved system efficiency through query optimization, distributed processing, and resource tuning
• Contributed to modernization initiatives, enhancing scalability, maintainability, and system reliability
• Mentored teams on TDD, system design, and engineering best practices

Domain: Credit Risk | Financial Reporting

• Designed and enhanced credit risk and financial reporting platforms
• Led system migration and modernization efforts to improve performance and scalability
• Collaborated with stakeholders to translate complex financial workflows into scalable technical solutions
• Reduced delivery timelines through process optimization and efficient system design

• Developed components for commodity trading and P&L systems
• Built backend services supporting trade lifecycle and pricing workflows

• Contributed to development of listed derivatives trading systems
• Worked on financial data processing and real-time system components

• Developed enterprise applications in document and workflow management systems