RHUTWIJ TULANKAR

• Leading data & API architecture, design, testing of Nielsen's Audio / Auto and podcast products, reporting directly to VP.
• Working on cost reduction on all Audio / Auto and podcast products, to increase profit margin, reduce maintenance overhead and tech-debt.
• Leading re-design of API layer, data layer and platform and infrastructure layers to consolidate technology stack and reduce costs.
• Working on consolidation of API gateway technologies and choosing the best option in terms of engineering cost and also to reduce contractual costs.
• Working 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.
• Working on creating 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.
• 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 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.
• Setup infrastructure for machine learning TensorFlow, Keras, PyTorch, MXNet, Spark ML, DeepLearning4j on EMR and Databricks.
• 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.
• Designing and implementing QA, data-quality, unit, integration testing framework for machine learning and data workflows to make pipelines and service resilient and fault tolerant.
• Coordinate 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.
• Report and document technical requirements, OKR's for the team and work with Manager to do R&R, scoping of project.
• Present and propose 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.
• Developed Anomaly detection project for run team, to detect fraudulent activity from users during workouts.
• Built, designed and delivered new realtime data pipeline for Analytics, Advertising and App teams.
• Requirement gathering from multiple stakeholders.

• Worked with analytics team at Sailpoint Technologies.Inc that does threat detection and peer group analysis in real time.
• Developed, designed an 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 web services based job runner to run analytics jobs.
• 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 peers 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 pipleline 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.
• Designed and developed monitory 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.

Role :

Building data pipeline (AWS Kinesis, S3), ETL & data transformations using Apache Spark, Warehousing with AWS Redshift database to power dashboards, unit testing with ScalaTest / JUnit / PHPUnit, adding features to Apache Solr with small Plugins and algorithm changes, collecting stats and feature engineering, making, managing and maintaining search and internal APIs and front end Applications.

Search & optimization projects:

• Worked on Apache Solr adding small features (Solr Plugins) during indexing and searching (Querying) to enhance search results and quality of product. Increased indexing speed on 10 million documents from 4hrs to 40 minutes by increasing indexing threads within Apache Solr and writing parallel program to index documents in parallel.
• Involved in scaling API for 12-13 million users having 170 Million request per day and 200k-300k request per second at peak times. Scale was achieved with AWS Elastic BeanStalk, Docker which were used to scale search engine Apache Solr with average latency of 100 milliseconds response time and maximum of 20 hosts. Web Server scale was achieved by tuning PHP-FPM and Linux kernels.
• Developed RESTful API in Java Jersey (Tomcat) and PHP.

Big data projects:

• Logging data project: Logged all click, conversion, impression events on the site with PHP application using AWS firehose fluming agent to send events to AWS kinesis to AWS S3 storage and AWS Redshift.
• Designed schema for Redshift.
• Worked on big data project to send job alerts to 5 million users. Customized Email job alerts for each user using Apache Spark, AWS EMR, Redis by generating unique user profiles (user search and click history) for each user based on user activity like searched keywords and click events, wrote program in Scala/Apache Spark and deployed on AWS EMR.
• Customized third party (publisher) searches to generate more CTR (click through rate) and revenue per visitor by providing user job recommendations based on click events.
• Worked on Databricks cloud to do some ETL and big data collection tasks using Apache Spark SQL, Parquet and Data Frames api.
• Created reporting dashboard system with Data Warehouse using AWS Redshift to show impressions, clicks and conversions at job level, ETL was performed with spark and loading some data from S3 directly.
• Collected data for machine learning converted to features and generic stats to train models for Classification, Regression, Clustering by running Spark jobs with scheduler.

Web projects:

• Top Spot Job project: Made Top Spot feature on site to show high value jobs on top of site.
• Built Mobile website & search API in PHP for publishers and jQuery/HTML/PHP front end pushed it to production.
• Created small API with Java Jersey for reporting system for jobs2careers client and used jQuery/PHP application to display it.

• Developed Mobile site api (Java/PHP).
• Worked on Job Importing system in Perl.
• Designed crawlers and feedsystem.
• Worked on reporting api's.