Vatesh Pasrija — CTO

I’m truly passionate about my work and always eager to connect with other leaders and developers. While I enjoy all aspects of my job, I think my favorite stage of a project is working with the client or business unit leader to understand their business objectives and develop to enhance business for them. As we go through that collaborative process, the ideas start to flow and that’s always the fun part. I’m always interested in hearing from friends, former colleagues or just interesting creative folk, so feel free to contact me if you’d like to connect. My critical role as Engineering Leader includes • Recruit, hire, mentor, develop and manage a best-of-class engineering team of software development managers and engineers responsible for developing and maintaining high-quality software. • End-to-end ownership of a technology team, including strong technical and business judgments for long-term architectural and business strategies that will accelerate time to market releases while incrementally moving our services towards the long-term vision. • Project management - prioritization, planning of projects and features, stakeholder management, and tracking of external commitments. • Management and execution against project plans and delivery commitments. Manage the day-to-day activities of the engineering team within an Agile/Scrum environment. • Engineering/Operational Excellence - monitoring & operating of production services by establishing system and product performance metrics. Follow a closed-loop regular review process for assessment and improvement. • Building exceptional relationships with internal and external customers, including discovering the true requirements, recommending alternative technical and business approaches, and leading engineering efforts to meet aggressive timelines with optimal solutions.

Stackforce AI infers this person is a SaaS and B2C engineering leader with a strong focus on machine learning and platform scalability.

Location: Seattle, Washington, United States

Experience: 13 yrs 6 mos

Skills

Engineering Management
Project Management
Machine Learning

Career Highlights

Led enterprise identity platform at Meta.
Improved recommendation model accuracy by over 400%.
Drove operational improvements for 500k+ TPS.

Work Experience

Amazon

Engineering Leader (8 yrs 10 mos)

Samsung Electronics

Software Engineer (5 mos)

Education

at Birla Institute of Technology And Science (BITS), Pilani

Vatesh Pasrija

CTO

Seattle, Washington, United States13 yrs 6 mos experience

AI ML PractitionerHighly Stable

Key Highlights

Led enterprise identity platform at Meta.
Improved recommendation model accuracy by over 400%.
Drove operational improvements for 500k+ TPS.

Stackforce AI infers this person is a SaaS and B2C engineering leader with a strong focus on machine learning and platform scalability.

Contact

er.vateshpasrija@gmail.com LinkedIn

Skills

Core Skills

Engineering ManagementProject ManagementMachine Learning

Other Skills

AJAXASP.NETAgile MethodologiesAlgorithmsAmazon Web Services (AWS)Artificial Intelligence (AI)Back-End Web DevelopmentCC++C/C++ STLChange managementCisco Certified Network Associate(CCNA)Computer EngineeringCore JavaCross-functional Team Leadership

About

Experience

Amazon

Engineering Leader

Jan 2013 – Nov 2021 · 8 yrs 10 mos · Seattle, Washington

As Engineering Leader, I supported multiple teams of Customer Title Relevance (CTR) org in Amazon Prime Video, which helps customers discover an enjoyable video to watch. These teams achieve their mission through building a personalization platform that includes owning end-to-end machine learning offline and online backend systems. This offline system includes data ingestion, feature engineering, experimentation, hyper-parameter optimization, evaluation, model training, and publishing, whereas online backend REST-based web services drive customer engagement within Prime Video. This system drove 80% of First-Discovery streams and 500K Monthly Active Customers by personalizing (title ranking/scoring) over 70% of carousels on Prime Video Homepage. The system was highly scalable and maintainable to supports over 300 million customers and 500K+ TPS worldwide.
Notable Achievements:
a) Improved the accuracy of recommendation model by over 400% and expanded it to over 100+ territories worldwide. This includes upgrading the recommendation models from matrix factorization methods to advanced neural network-based models, Resnets/RNN. Also built cutting-edge recommender systems for complex cold-start problem.
b) Increased the rate of experimentation from 4-5 significant experiments a year to over 20, leading to hundreds of thousands of new customers to our services as measured through A/B testing (i.e., incremental).
c) Repurposed the model to personalize various carousels and customer experiences, driving over 80% of first discovery events and Amazon Originals.
d) Drove operational improvements to serve over 500k+ TPS at a latency of under 10ms P99 (from 2-3k at ~300ms)
e) Launched Profiles to enable Prime Video customers to share their accounts with their family members and enabled personalization of each of their profiles individually. This enabled personalized profiles for over 150+ million users within the first-year and significantly increased in overall user-engagement

Machine LearningNeural NetworksData EngineeringREST-based web servicesPersonalization platformEngineering Management