• Designed and operated RESTful control plane APIs for MediaConvert (high-scale video transcoding service), processing millions of daily job orchestration requests across distributed microservices (AWS API Gateway, Lambda, DynamoDB, SQS, S3, ECS/EC2).

• Led cross-team initiative on Step Functions and MediaConvert integration across CreateJob and Probe APIs, driving ~20% user growth; mentored engineers to production readiness.

• Delivered high-visibility integrations for NAB 2024 by collaborating with Solutions Architects, Product, and partner teams; recognized for 'Customer Obsession' and 'Invent & Simplify' Leadership Principles during quarterly business reviews.

• Designed and launched a secure file-sharing API for customer support investigations, reducing resolution time from days to hours while meeting strict AppSec and compliance requirements.

• Built fraud containment, authorization enforcement, and fast-fail validation systems, restricting 500+ high-risk accounts and preventing thousands of failures per month while reducing wasted compute.

• Partnered with applied science teams to productionize ML inference pipelines; re-architected SageMaker-hosted model evaluation workflows, cutting inference evaluation latency by ~96%.

• Contributed to an Innovation Award-winning project by building vector-based video search using SigLIP embeddings and scalable inference pipelines.

• Developing a Customer Data Platform to onboard, amplify & monetize 1st party data to target look-alike users for advertisements

• Led an international team of 3 members to automate the amplification of 1st party data of an online delivery platform over 6 sprints in an Agile development process. This entailed daily standups, presentations to stakeholders, code reviews, and system testing

• Collaborated with Engineering, Product Management & Data Operations teams to develop an algorithm using 3rd party Graphs

that helped our data pipelines more efficiently process user data and enhance the campaign creation rate by 20%

• Leveraged Spark, Kafka & Microservices to ingest real-time advertising data into a low-latency data store for Bid Optimization

• Implemented Data Deduplication feature that reduced business intelligence data by 60%

• Trained and mentored new interns & full-timers in improving their Spark Scala skills & get familiar with our big data pipelines

Worked on a NASA - CMU collaboration project aimed at developing a web application to provide NASA crucial insights on the advancement of science and research focus areas in the Earth Science domain. As a part of the project, I designed and developed a standalone dockerized Java application that facilitates the training of topic model - Labelled LDA (Latent Dirichlet Allocation) using Stanford Topic Modelling Toolbox (TMT). This involved data extraction, feature engineering, NLP based Data analysis of research publications.

Technologies Used: Scala, Java with Play framework, MongoDB