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Designed and successfully led initiatives on Smart Buildings, Smart Transportation, and Traffic Management systems, which have been implemented across geographies, in Singapore, the United States, and India. I take pride in the fact that the solutions have, in most part, been thorough, impactful, cost-effective, and sustainable. It has also been my constant endeavor to build AI-powered organizations to enable them to scale up to their highest digital potential.

Dec 2020 – Present | AI-IN-A-BOX

Engineered a full-stack Data Science platform that provides adaptable tooling for Data Scientists, domain experts, and developers, vital for successful AI and Data Science implementations.

The AI-in-a-Box solution accelerates the adoption of AI among start-ups, SMEs, and larger enterprises. A turnkey solution that includes everything from provisioning and deployment to managed services and training. On-premises, on the Edge, with all the advantages of cloud.

Vinod Bijlani, Innovator, Strategist & AI Leader
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Apr 2020 – Sept 2020 | RETURN-TO-WORK

Conceptualised and designed the development of Smart Building solutions that have helped organizations to cautiously reopen workspaces and bring their workforce safely back to office during the pandemic normal. 

The Return-to-Work solution, an entry to exit Workplace Transformation solution, is aimed at safeguarding employee health, improving the overall office experience, and reducing energy costs by working towards net zero carbon emissions. My experience with Smart Buildings has been published in the Business Times, Singapore. Find the complete report here.

Vinod Bijlani, Innovator, Strategist & AI Leader

Aug 2019 – Aug 2020 | AIOps - AUTONOMOUS DATA CENTRE

Designed an AIOps platform with end-to-end service monitoring, predictive management, and full-stack visibility across hybrid cloud environments.

Worked with internal HPE teams (Infosight, Aruba Central) and external partners (Splunk, Dynatrace) to implement a scalable AIOps solution that monitors every component of an IT solution, starting from data centers and infrastructure to middleware, network, and applications, across cloud platforms. The solution has been successfully implemented by HPE support centres and various customers. My thoughts on how AIOps uses machine learning and deep learning to simplify IT operations management while accelerating and automating problem resolution in complex modern IT environments has been published here.

Vinod Bijlani, Innovator, Strategist & AI Leader


I directed and executed the Fusion Analytics for Public Transport Emergency (FASTER) project for the improvement of Singapore’s MRT services. The goal for this project is to provide optimized response plans in case of transit incidents. I designed a system which implemented complex fusion analytics based on WIFI, transit fare card and video data to identify incidents, predict the impact of incidents and generated multiple response plans using optimization algorithms. The fusion algorithms utilized WIFI and ATS data to determine Train and commuter KPIs for the overall transit network. I led a team of architects and developers based in India, Korea and Singapore to iteratively design, develop and deploy the models generated by data scientist team.

Vinod Bijlani, Innovator, Strategist & AI Leader


I designed and implemented an advanced traffic incident detection and response system that featured state of the art streaming architecture, AI and IoT technologies. Based on real time situations and predictions, DECON optimizes traffic flow and safety by adjusting traffic signal times, variable message signs (VMS) and variable speed limits.

Vinod Bijlani, Innovator, Strategist & AI Leader


Led the architecture and design of core components for IOC - Traffic Device Management, Highway Management, Traffic Prediction, Data Expansion, and Bus Arrival Prediction. worked with the Product Management team to define the product roadmap and release strategy for the Transportation component of IOC. led the product delivery team comprising of 30 developers and testers across the US, India, and Ireland.

Vinod Bijlani, Innovator, Strategist & AI Leader


I designed and implemented a system having an integrated view of all signs along with network view and advanced analytics tools to automate updates to the speed limits and travel messages for drivers. This engagement with NJTA leadership helped us displace the incumbent, Delcan and they signed a multi-year implementation agreement with IBM to enhance the Authority’s traffic management system. This was the first instance worldwide that IBM's Transportation analytics solution was implemented to provide a holistic view for monitoring traffic events and conditions on two of the world's most heavily traveled highways and busiest toll roads in the United States.

Vinod Bijlani, Innovator, Strategist & AI Leader

Aug 2012 - Feb 2014 | TRANSIT ANALYTICS

I was the Architect for the Transit Analytics component of IBM Intelligent Transportation. I developed a Service Oriented Architecture which supported interfacing with multiple bus operators, traffic systems and third party applications. I created an architecture having the following components - Arrival Prediction, Historic Calculation, Bus Information, Traffic Information and Bus Operations.

Vinod Bijlani, Innovator, Strategist & AI Leader

Jan 2010 - Jun 2011 | BUS ARRIVAL PREDICTION

I drove a 150% improvement on the accuracy of estimated bus arrival times, from +/-1 minutes error down to +/- 30 seconds. Conventionally, this would not have been possible, due to the highly variable nature of road conditions. I successfully combined Traffic Prediction models with GPS information from buses to create a dynamic system capable of processing massive variable data and producing predictions that are accurate within seconds of bus arrival.

Vinod Bijlani, Innovator, Strategist & AI Leader

Jun 2008 - Aug 2013 | TRAFFIC PREDICTION

I led of a team of application developers, administrator,s and deployment experts to design, develop and deploy Traffic Prediction at Singapore LTA and NJTA. Post the successful client implementations, I led the productization of the Traffic Prediction component. In the planning phase, I worked with the Intelligent Transportation product manager to define release-based requirements. I worked with the usability expert to design the administrator and operation user interface. I created a UI design which supported global languages and could also be used by visually challenged.

Vinod Bijlani, Innovator, Strategist & AI Leader


Transport for London was interested in implementing an Automatic Incident detection system. Typically incidents were being reported in about 10 to 15 minutes. This delay at times resulted in heavy congestion. TFL wanted to reduce the incident detection time to no more than five minutes. I worked with the IBM Research team to create the models for Incident Detections and Impact Predictions. Based on the requirements and models developed, I defined the following components - Traffic Information Hub, Traffic Prediction, Incident Detection Module, Incident Severity Evaluation, Traffic Simulator, and Decision Support System Optimizer. I led the implementation team in delivering a Cloud-based solution.

Vinod Bijlani, Innovator, Strategist & AI Leader

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