vijay r

CTO

Bengaluru, Karnataka, India15 yrs 6 mos experience

Key Highlights

Over 12 years in software development.
Expert in full-stack and microservices architecture.
Significant contributions to major tech companies.

Stackforce AI infers this person is a SaaS-focused full-stack engineer with expertise in scalable systems and microservices architecture.

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Skills

Core Skills

SearchMicroservicesFull-stack DevelopmentData Consistency And Reliability

Other Skills

Amazon CloudFrontAmazon DynamodbAmazon EBSAmazon EC2Amazon S3Amazon SQSApache ThriftCassandraElastic Stack (ELK)ElasticsearchHadoopJ2EE Web ServicesJavaJavaScriptNode.js

About

With over 12 years of experience in the software development industry, I’ve had the opportunity to design and implement scalable, high-performance systems across some of the world’s leading tech companies—Google, Atlassian, Salesforce My expertise lies in building and optimizing full-stack applications and architecting distributed systems that power today’s enterprise solutions. Throughout my career, I’ve consistently driven innovation in the following areas: Full-stack development: Expertise in creating end-to-end solutions, from front-end user interfaces to backend services. Microservices architecture: Designing scalable, maintainable microservices for cloud-based environments. Cloud technologies: Skilled in deploying and managing cloud infrastructures with AWS, GCP, and Azure, and leveraging containerization with Docker and Kubernetes. Data consistency and reliability: Experience building distributed, fault-tolerant systems to handle massive amounts of data with a focus on high availability. I’ve been fortunate to contribute to projects that have had a tangible impact: At Google, I worked on optimizing large-scale data-driven services that powered essential business functions, improving both system efficiency and processing times. At Salesforce, I played a pivotal role in enhancing integrations between enterprise applications, resulting in improved performance for clients. At Atlassian and Intuit, I worked on full-stack applications that streamlined workflows for developers and improved customer-facing products.

Experience

Google

Engineering Leader - Google Search

Sep 2024 – Present · 1 yr 6 mos · Karnataka, India · Hybrid

Championed integration of generative AI capabilities (e.g., LLMs) into Search to anticipate user intent and deliver richer answers.
Led cross-org product initiatives spanning Search, Maps, and Travel, accelerating feature adoption by X%.
Drove international rollout strategy for AI features, optimizing for localization and geo-specific relevance.
Conducted A/B tests and launched data-informed features that improved local search engagement by Y%.
Mentored junior PMs and collaborated with research teams to fine-tune relevance models based on user signals.

Spring BootSearchNode.jsMicroservices

Atlassian

Principal Software Engineer

Feb 2024 – Aug 2024 · 6 mos · Bengaluru, Karnataka, India · Remote

At Atlassian, I contributed to the development of Jira Service Management (JSM), a leading IT Service Management (ITSM) platform that helps IT teams provide seamless, high-quality service experiences. My role was focused on enhancing the platform’s functionality to improve how teams manage service requests, incident management, problem resolution, and change management. Some of my key contributions included:
Developing automation workflows: Implemented advanced automation features to streamline repetitive tasks, such as automatic ticket routing, escalation processes, and SLA enforcement, which significantly reduced manual workload for IT teams.
Building scalable microservices: Architected microservices that supported critical functions like incident tracking, change approvals, and service request automation, ensuring the platform could scale with the needs of large enterprise customers.
Enhancing integrations: Contributed to third-party integrations that allowed seamless connectivity between JSM and other platforms (like Slack, Confluence, and Opsgenie), improving team collaboration and communication during critical incidents.
Improving incident management: Worked on features that enhanced incident resolution by developing real-time notifications, post-incident reports, and analytics dashboards to help teams better understand and resolve recurring problems.
Optimizing performance: Led performance optimization initiatives to ensure the platform could handle high volumes of service requests without sacrificing speed or user experience, improving overall system reliability.
By leveraging my full-stack expertise and a deep understanding of ITSM processes, I helped to improve key aspects of the JSM product, allowing teams to deliver better service, reduce downtime, and foster collaboration across IT and DevOps functions.

Spring BootSearchFull-stack developmentMicroservices

Salesforce

LMTS

Apr 2022 – Jan 2024 · 1 yr 9 mos · Bengaluru South, Karnataka, India · Hybrid

I played a pivotal role in the evolution and scalability of the SFDocs Search Engine at Salesforce, contributing to its growth from handling 100 million searches to over 1 billion searches per day within a span of 2 years. Key accomplishments include:
Platform Foundation and Scaling: Designed and built the foundational architecture that enabled the SFDocs Search Engine to scale from handling millions to billions of searches daily. Implemented robust infrastructure improvements and optimizations to manage high-volume traffic efficiently.
Feature Engineering and Training Pipelines: Developed a feature engineering platform encompassing logging and training pipelines. This enabled the extraction and utilization of result-cluster and whole-page features, enhancing the relevance and quality of search results across diverse content verticals.
Experimentation Enablement: Empowered partner teams within Search to experiment with over 50 content-vertical categories over 2 years. Facilitated continuous improvement in Search Engine Results Page (SERP) quality and ranking relevance through iterative testing and data-driven optimizations.
Latency Optimization: Led multiple initiatives to optimize latency in the SERP blending stack, achieving a significant 14% reduction (~70ms P95) in latency across critical paths for over 1 billion daily searches. Implemented real-time component-level latency monitoring and tracing to maintain high performance in a complex, parallel processing environment.
Performance Profiling and Optimization: Conducted regular CPU and I/O wait performance profiling to identify and address inefficiencies such as improper large-object move semantics, overuse of heap memory data structures, and inefficient RPC request handling. This proactive approach ensured continuous improvement and stability in search performance.

Spring BootAmazon DynamodbElasticsearchAmazon EC2Amazon CloudFrontHadoop+9