Vikas Sangwan

Software Engineer

Bengaluru, Karnataka, India8 yrs 5 mos experience
Most Likely To SwitchAI ML Practitioner

Key Highlights

  • Led development of centralized data lake at Adobe.
  • Architected high-performance AI recommendation engine at Tabsquare.ai.
  • Designed large-scale transcoding system at Dailyhunt.
Stackforce AI infers this person is a SaaS expert with a strong focus on AI and machine learning infrastructure.

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Skills

Core Skills

Machine LearningSoftware Engineering

Other Skills

API DevelopmentAWS LambdaAdobe AnalyticsAlgorithm DevelopmentAlgorithmsAmazon Web Services (AWS)Apache KafkaApache SparkApplication Programming Interfaces (API)Artificial Intelligence (AI)Cloud DevelopmentComputer VisionCritical ThinkingData AnalyticsData Pipelines

About

Senior Software Engineer & Architect | Specializing in Scalable AI Systems & Machine Learning Infrastructure A results-driven Software Engineer and Architect with 8+ years of experience building and scaling the foundational systems that power intelligent applications. My expertise lies at the intersection of high-performance software engineering and practical machine learning, designing robust infrastructure that turns data into actionable insights and drives significant business impact. My core focus is on architecting and implementing end-to-end AI solutions—from data pipelines and model training to deployment and scaling—ensuring they are not just theoretically sound but also production-ready, efficient, and reliable. Key Achievements: At Adobe: Led the development of a centralized data lake, serving as the single source of truth for ML models. This initiative was a key enabler for advanced analytics and personalization features, directly contributing to 15% revenue growth while reducing data processing load by 18%. At Tabsquare.ai: Architected and built a high-performance AI recommendation engine from the ground up. Optimized the machine learning lifecycle to achieve 78.3% top-3 prediction accuracy, which increased platform revenue by 8.9%. At Dailyhunt: Designed and engineered a large-scale, distributed animation transcoding system, leveraging parallel processing to efficiently handle 500,000+ video assets daily, ensuring seamless user experience. Technical Expertise: AI/ML & Data Science: Machine Learning Systems, Recommendation Engines, Data Pipelines, Model Optimization, MLOps, RAG-based systems Software Engineering: Distributed Systems, High-Performance Architecture, OOP, Design Patterns, System Design Cloud & Infrastructure: Kubernetes, Docker, CI/CD, AWS/GCP, Microservices Languages & Tools: Python, Flask, Snowflake, Postgres, Spark, Kafka I am passionate about building intelligent, scalable systems that solve complex problems. I thrive on connecting deep technical architecture with overarching business goals. Let's connect to discuss the future of AI-powered systems! 🚀

Experience

8 yrs 5 mos
Total Experience
2 yrs 1 mo
Average Tenure
4 yrs 1 mo
Current Experience

Adobe

Software Development Engineer 3

May 2022Present · 4 yrs 1 mo · Hybrid

Dailyhunt

Senior Software Engineer

Apr 2021Jun 2022 · 1 yr 2 mos · Bengaluru, Karnataka, India

Tabsquare.ai

Software Engineer (Data Science)

Dec 2018Apr 2021 · 2 yrs 4 mos · Bengaluru, Karnataka, India

  • Dish recommendation system :
  • Designed and developed a RESTful service which replaced the monolithic service and resulted in
  • reducing the response time by 80%(2 seconds to 400 MS)(P95).
  • The service was scaled to serve 420k+ users per month by doing database query optimizations and
  • using Elasticsearch cluster.
  • Built an end-to-end recommendation system for AI Engine achieving average top3 accuracy of 78.3%
  • in dish adoption and contributed to 8.9% increase in the revenue.
  • Designed and developed the JSUP cold start system that helped to provide AI services to the end user
  • from Day one of onboarding.
Machine Learning SystemsRecommendation EnginesData PipelinesModel OptimizationMLOpsRESTful services+4

Untrodden labs

Machine Learning Engineer

Dec 2017Nov 2018 · 11 mos · New Delhi Area, India

  • Smart X :
  • Built a facial recognition system for hassle-free attendance management that could be installed in
  • corporate offices.
  • Improved the scalability of system to recognize upto 200 employees and reduced the recognition time
  • by 60%.
  • Muffin :
  • Built an end-to-end pipeline that included Named Entity Recognition for the muffin bot and provided
  • several services like meeting booking,reminders etc.
  • Replaced the old NER method with StanfordNER that uses Conditional Random Fields which improved
  • the performance from 82.3% to 89.5%.
  • Technologies & Tools : Python, OpenCV, Dlib, Tensorflow, Odroid.
Facial RecognitionNamed Entity RecognitionPythonOpenCVDlibTensorflow+2

Education

Maharaja Agrasen Institute Of Technology, Delhi

Jan 2013Jan 2017

Army public school

Schooling

Jan 2008Jan 2013

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