RHUTWIJ TULANKAR

* Led data & API architecture, design, testing of Nielsen's Audio / Auto and podcast products, reporting directly to VP (Team size : 6).

• Delivered cost reduction on all Audio / Auto and podcast products, to increase profit margin, reduce maintenance overhead and tech-debt.

• Led re-design of API layer, data layer and platform and infrastructure layers to consolidate technology stack and reduce costs.

• Consolidated API gateway technologies and choosing the best option in terms of engineering cost and also to reduce contractual costs.

• Worked with Product and cross functional teams to enhance the product, customer facing team to get customer requirements and then adding those features to products.

• Responsible PI Plans for the team and OKR's, mentoring team members. Design cross platform architecture that is cost efficient, modern, scalable and futuristic for platform to be used for ML application to broaden product and solutions suite.

• Created modern solutions that will bring future revenue and automation that will free up limited capacity into other areas.

• Led, designed and developed comprehensive unit test, integration test, regression test, performance test, behavioral test , data quality test generic Framework for API, ETL and ML/DL applications.

• Led and developed orchestration and data ETL pipelines for making data rich. (led team of 10-15 engineers in terms of technical direction, technical doc, R&R, architecture, project management, mentorship, reporting to Sr Director Audience Intelligence)
• Developed feature generation jobs for machine learning models at scale.
• Led the design and architecture of feature store, Deep/Machine learning API's to provide cross-platform solutions for Ad/Marketing teams focusing on personalization, placements, Augmented reality. Also developing common ML/DL services, products, to be integrating with Ads/ Marketing teams to do Omni channel advertising, multi channel advertising, risk analysis across psychographic, economic, demographic , geographic and other behavioral dimensions.
• Architected and developed Unified Artificial Intelligence framework for Samsung used my multiple teams.
• Cross collaborated multiple Samsung Divisions to get requirements, propose unique solutions in cross functional setup with global teams.
• Setup and designed cross platform framework for machine learning TensorFlow, Keras, PyTorch, MXNet, Spark ML, DeepLearning4j
• Developed and designed data insights framework for marketing team to help them spend more efficiently across different marketing channels, only diverting spend to channels with higher conversion, demographic groups and specific audiences.
• Developed Targeting audience module with Spark and Tensorflow with attribution. Mentor and work with data scientist to do programmatic and predictive models on campaign spend and help Ad's team in real time bidding using deep learning strategies.
• Led and work with data scientist and data engineers to productionize both data engineering and machine learning models at scale, setting up HA services with monitoring.
• Designed and implemented QA, data-quality, unit, integration testing framework for machine learning and data workflows to make pipelines and service resilient and fault tolerant.
• Coordinated with different stakeholders and do requirement gathering and knowledge transfer across my team, so that our goals are on track and engineering tasks align with business needs.
• Reported and document technical requirements, OKR's for the team and work with Manager to do R&R, scoping of project and timelines.
• Presented and proposed new research and business ideas, direction and technical execution plan and strategies

Responsibilities:

• Write ETL, Machine Learning Spark/Hive jobs for analytics and core teams, for targeting Ads per user based on user features.
• Maintain and write airflow DAG's.
• Led and developed Scala microservices for nutrition and run teams in Scala/Java.
• Developed load test microservices with Locust framework.
• Lead and worked on Kubernetes and infrastructure deployment.
• Used ML techniques and knowledge graph to improve logistics and inventory management for underarmour.
• Developed Anomaly detection project for run team, to detect fraudulent activity from users during workouts. Developed PoC feature for fitness food app with NLP and machine learning techniques to add voice command functionality to log foods and ability to user to interact with voice.
• Built, designed and delivered new realtime data pipeline for Analytics, Advertising and App teams.
• Requirement gathering from multiple stakeholders and mentoring junior engineers.

• At Sailpoint, I was one of two data / ML engineers that co-designed, architected & productionized the data pipeline and ML approaches for Sailpoints new AI solution called Identity AI, to automate task done by Sailpoint Administrator, along with risk analysis and threat detection / analytics capabilities, launched the solution with use-case studies from incubation to GA to having 6 live customers at that time in under 1.4 years.
• Worked with analytics team at Sailpoint Technologies.Inc to do threat detection and peer group analysis in real time.
• Developed, designed and scaled an multi-tenant event driven architecture for big data pipelines for real time streaming and batching of events, creating historical views of data for visualization with Apache Flink and Apache Spark.
• Designed and developed scalable web services • based job runner to run analytics jobs for mutiple tenants. Developed and orchestrated machine learning pipelines for real time analysis of data, detecting user groups and threats by using machine learning methodologies and techniques like pattern recognition, classification, clustering and prediction engines.
• Built recommendation engine for recommending user suggestions to enhance security and recommend peer groups.
• Led tuning of machine learning models for real time and batch threat detection, risk assessment and request monitoring and management using Apache mahout, Deeplearning4j and Apache Spark MLlib using algorithm like Eclat, Louvain etc.
• Built real time data pipeline using AWS Lambda, Apache Spark, Apache Flink, ElasticSearch for data ingestion of various events and identities over period of time.
• Created batch, speed processing and service layers for rapid ingestion of data, using historic data to make predictions and identify as well as predict behavior.
• Collaborated with solution architects to create multi-tenant architecture for all of Sailpoint current products to harvest data from the different data sources and do analysis and threat detection on harvested data in real time.
• Evaluated big data technologies to carry out ETL processes before analysis of data.
• Made pipeline architecture scalable, fault tolerant and real time.
• Improved analysis techniques to suite business use cases. Unit testing, performance testing, regression testing infrastructure to ensure continuous integration. Also lead development of data quality and governance framework.
• Designed and developed monitoring systems and workflow for data pipelines and analysis and managing job deployment infrastructure.
• Worked on data quality assurance during the CI/CD release process, to make sure the transformation logic of ETL jobs doesn't affect data is displayed to our customers in any wrong way, so that data pipeline artifacts are shipped along CI/CD pipeline with high degree of confidence assuring data-quality along with service scale and up time.
• Jenkins configuration and test suites.

• Worked as lead engineer doing personalization of search and monetization designing, developing data and ML solutions to improve relevance and click through rate.
• Architected and developed search API with custom Solr plugins to expand keyword search using Word2Vec and profiling ML approaches to service users in real-time, with response time less than 100ms ,having transaction / query volume of 200/300k queries per second at peak times.
• Designed and developed email system that delivered • personalized emails to 5 million users everyday. Architected and developed data pipelines and data cleaning systems along with crawling systems for ingestion of data (Spark, Kinesis).
• Worked and improved the catalog importer for database. Co-designed and developed job history data warehouse (Star Schema Data modelling with Redshift) and prediction algorithm with regression model and job classifier to predict CPC.
• Designed and developed Top Spot project to have unique backend API for Top rated jobs by employers to increase CTR and pint pointed targeting of specific audiences, using audience analytics and measurements.
• Modernized reporting and analytics backend API's.

• Developed Web Mobile App for Jobs2careers from design to development and productionized it.
• Recalibrated and miniaturized the existing job import system that was used for indexing / cataloging the Ad inventory for just Mobile Ads, improved processing time and making system modern in terms of code.
• Designed crawlers and feed system that were used to scrape jobboards.
• Developed new backend search API (fork of existing miniaturized) and reporting API that powered the Mobile App.