Princeton Junction, New Jersey, United States
Rakesh is an engineering leader with expertise in low-latency computing, high-frequency event processing, and large-scale distributed systems. An engineer at heart, he stays hands-on and lead research at the intersection of CPU & GPU query engines. With a career spanning Video Games, Fintech, and Database Engineering, he has built mission critical infrastructure, from high performance high-volume (billions/sec) data pipelines to query engines for time-series databases. Over the past decade, being a senior engineering leader, he has driving technical vision and execution for global teams. He leads global organizations across various product lines and has experience delivering strategic impact in Observability, Cybersecurity, Revenue & Monetization, and AI. He works closely with executives to align technical roadmaps with business objectives while mentoring senior leaders and principal engineers to develop high performing teams. Rakesh is a C++ expert and a passionate engineer who is actively involved in the engineering community, and contributes to open source initiatives in high-cardinality data processing. His current focus lies in distributed stateful lambda processing systems for low-latency product space.
Director of Engineering, ENG Atlas Core, MongoDB
We are the creators of the M3 time series database. We provide the metrics platform powering the observability initiative at Uber. Our systems ingest billions of events every second and enables monitoring and detection mechanisms that extract critical signals. If you are interested in joining my team (or interested in observability), please feel free to reach out.
Over the years, I have worked on many teams at Bloomberg. I managed a team of engineers leading the research, architecture design and development of Complex Event Processing (CEP) Engine for our core Trade Automation business. Our client facing automation product has direct revenue implications for Bloomberg. We provide our clients an automated workflow to move billions of dollars of their trade activities. I worked with our colleagues to lead our org's vision and mission and partner with Product and cross-functional teams to constantly adjust and optimize the product's impact to deliver a first-class automation workflow for Bloomberg clients. Prior to Trade Automation, I worked in the Tickerplant group dealing with high-volume low-latency infrastructure moving 100s of billions of data points in a day at times during peak time, the system handles 10s of millions of market events per second with low-latency SLAs. I have experience designing and coding all major pillars of TickerPlant;data distribution, critical signal/field computation and in-house datastore. And of course, I am an Engineer first and a Manager second and I have strong interest in research and development of data structures and algorithms that drive the critical low-latency systems. I have particular interest in lockless data structures, memory allocators and cache-oblivious data structures. I also keep up-to-date with the latest publications and activities in the parallel computing journals. Recently, I have gotten interested in learning functional programming in Haskell.
Work on the big data platform for DRMC risk system.
Analyze signals, risk patterns and limit break predictors for fixed income trading. Model engineering and portfolio analytics for fixed-income(IRD, CDS, Bermudan Exotics). Developed various components for Cross-one trading systems. Over the years moved into systems that provide services to traders and risk managers. Developed various data analysis subsystems of the risk engine dealing with feeds from Bloomberg and Reuters.