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Saurabh Baji Phones & Addresses

  • 10036 NE 147Th St, Bothell, WA 98011
  • Seattle, WA
  • Kirkland, WA
  • Redmond, WA
  • Bellevue, WA
  • Austin, TX
  • Jersey City, NJ
  • 2100 3Rd Ave APT 603, Seattle, WA 98121

Work

Company: Amazon Mar 2012 Address: Seattle, WA Position: Software dev manager, elastic mapreduce, aws

Education

Degree: MS School / High School: The University of Texas at Austin 2004 to 2006 Specialities: Computer Science

Skills

Distributed Systems • Software Development • Hadoop • Agile Methodologies • Amazon Web Services • Java • Mapreduce • Software Engineering • Algorithms • Big Data • Web Services • Scalability • Linux • Sql • Object Oriented Design • Integration • Perl • C++ • Python • Machine Learning • Software Design • Eclipse • Scrum • E Commerce • Rest • Git • Lucene • Messaging Systems • Classification • Ruby • Hive • Bash • Messaging • Hibernate • Architecture • Oop • Postgresql • High Performance Computing • Apache Pig • Javascript • Design Patterns • Spring • Multithreading • Apache Spark • Presto

Languages

Hindi • Marathi • Gujarati

Industries

Computer Software

Resumes

Resumes

Saurabh Baji Photo 1

Vp, Ai And Data

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Location:
Seattle, WA
Industry:
Computer Software
Work:
Amazon - Seattle, WA since Mar 2012
Software Dev Manager, Elastic MapReduce, AWS

Amazon.com - Seattle, WA Feb 2010 - Mar 2012
Software Development Manager, Amazon Global

Amazon.com Feb 2009 - Feb 2010
Software Development Engineer / Manager, Performance Management Systems (PMANS)

Amazon.com Jul 2007 - Feb 2009
Software Development Engineer

Amazon.com Jul 2006 - Jul 2007
Software Development Engineer
Education:
The University of Texas at Austin 2004 - 2006
MS, Computer Science
University of Mumbai 2000 - 2004
BE, Computer Engineering
Gokhale High School
Sathaye College
Veermata Jijabai Technological Institute, Mumbai
Skills:
Distributed Systems
Software Development
Hadoop
Agile Methodologies
Amazon Web Services
Java
Mapreduce
Software Engineering
Algorithms
Big Data
Web Services
Scalability
Linux
Sql
Object Oriented Design
Integration
Perl
C++
Python
Machine Learning
Software Design
Eclipse
Scrum
E Commerce
Rest
Git
Lucene
Messaging Systems
Classification
Ruby
Hive
Bash
Messaging
Hibernate
Architecture
Oop
Postgresql
High Performance Computing
Apache Pig
Javascript
Design Patterns
Spring
Multithreading
Apache Spark
Presto
Languages:
Hindi
Marathi
Gujarati

Publications

Us Patents

Adaptive Regionalization For Transit Characteristic Prediction

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US Patent:
8504485, Aug 6, 2013
Filed:
Mar 4, 2010
Appl. No.:
12/717791
Inventors:
M. Christopher Wenneman - Seattle WA, US
Benjamin Elliott Pew - Seattle WA, US
Llewellyn W. Bezanson - North Bend WA, US
Girish S. Lakshman - Samammish WA, US
Saurabh D. Baji - Redmond WA, US
Assignee:
Amazon Technologies, Inc. - Reno NV
International Classification:
G06Q 30/00
US Classification:
705338
Abstract:
A method and system for transit characteristic prediction. In one embodiment, a method may include determining respective transit latencies from a source location to a number of destination locations, and grouping the destination locations according to a fitness function into a number of subsets corresponding to respective geographical regions. The grouping may involve a series of divisions and combinations of potential regions to form a plurality of sets of potential regions. Each set of potential regions may be evaluated using the fitness function, and the set with the better fitness score may be selected. The method may also include dynamically updating the respective transit characteristic, regrouping the regions, and reselecting a set of potential regions based on empirical transit data.

Automatic Scaling Of Resource Instance Groups Within Compute Clusters

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US Patent:
20210392185, Dec 16, 2021
Filed:
Jun 18, 2021
Appl. No.:
17/352065
Inventors:
- Seattle WA, US
Luca Natali - Kirkland WA, US
Bhargava Ram Kalathuru - Seattle WA, US
Saurabh Dileep Baji - Seattle WA, US
Abhishek Rajnikant Sinha - Bellevue WA, US
Assignee:
Amazon Technologies, Inc. - Seattle WA
International Classification:
H04L 29/08
H04L 12/26
H04L 12/24
G06F 9/50
Abstract:
A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

Method And System For Artificial Intelligence Based Video Game Testing

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US Patent:
20210089433, Mar 25, 2021
Filed:
Sep 21, 2020
Appl. No.:
17/027604
Inventors:
- San Francisco CA, US
Shuo Diao - Daly City CA, US
William Harris Kennedy - San Mateo CA, US
Jason Aaron Greco - San Francisco CA, US
Saurabh Dileep Baji - Bothell WA, US
Danny Lange - Sammamish WA, US
International Classification:
G06F 11/36
G06N 20/00
A63F 13/69
Abstract:
A method for evaluating a build of a computer-implemented game is disclosed. An evaluation request is received. The evaluation. request includes an identification of the build, data describing one or more behaviors for the build, and data describing one or more tests. One or more simulations of playing of the build are performed using the one or more behaviors. One or more metrics ate extracted from the simulations. Each of the one or more metrics measures an aspect of the computer-implemented game. One or more tests are applied to the one or more metrics to evaluate an adherence of the build to the one or more tests. A display of the evaluation is caused to be displayed in a user interface of a client device.

Automatic Scaling Of Resource Instance Groups Within Compute Clusters

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US Patent:
20200204623, Jun 25, 2020
Filed:
Feb 28, 2020
Appl. No.:
16/805412
Inventors:
- Seattle WA, US
Luca Natali - Kirkland WA, US
Bhargava Ram Kalathuru - Seattle WA, US
Saurabh Dileep Baji - Seattle WA, US
Abhishek Rajnikant Sinha - Bellevue WA, US
Assignee:
Amazon Technologies, Inc. - Seattle WA
International Classification:
H04L 29/08
H04L 12/26
H04L 12/24
G06F 9/50
Abstract:
A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

Automatic Scaling Of Resource Instance Groups Within Compute Clusters

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US Patent:
20180109610, Apr 19, 2018
Filed:
Dec 18, 2017
Appl. No.:
15/845855
Inventors:
- Seattle WA, US
LUCA NATALI - KIRKLAND WA, US
BHARGAVA RAM KALATHURU - SEATTLE WA, US
SAURABH DILEEP BAJI - SEATTLE WA, US
ABHISHEK RAJNIKANT SINHA - BELLEVUE WA, US
Assignee:
Amazon Technologies, Inc. - Seattle WA
International Classification:
H04L 29/08
G06F 9/50
H04L 12/26
H04L 12/24
Abstract:
A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.

Fault Tolerant Distributed Tasks Using Distributed File Systems

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US Patent:
20170249215, Aug 31, 2017
Filed:
May 15, 2017
Appl. No.:
15/595732
Inventors:
- Seattle WA, US
Rejith George Joseph - Seattle WA, US
Bandish N. Chheda - Seattle WA, US
Saurabh Dileep Baji - Seattle WA, US
International Classification:
G06F 11/14
G06F 9/50
G06F 11/34
G06F 11/20
Abstract:
Data files in a distributed system sometimes becomes unavailable. A method for fault tolerance without data loss in a distributed file system includes allocating data nodes of the distributed file system among a plurality of compute groups, replicating a data file among a subset of the plurality of the compute groups such that the data file is located in at least two compute zones, wherein the first compute zone is isolated from the second compute zone, monitoring the accessibility of the data files, and causing a distributed task requiring data in the data file to be executed by a compute instance in the subset of the plurality of the compute groups. Upon detecting a failure in the accessibility of a data node with the data file, the task management node may redistribute the distributed task among other compute instances with access to any replica of the data file.

Automatic Scaling Of Resource Instance Groups Within Compute Clusters

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US Patent:
20160323377, Nov 3, 2016
Filed:
May 1, 2015
Appl. No.:
14/702080
Inventors:
- Seattle WA, US
LUCA NATALI - KIRKLAND WA, US
BHARGAVA RAM KALATHURU - SEATTLE WA, US
SAURABH DILEEP BAJI - SEATTLE WA, US
ABHISHEK RAJNIKANT SINHA - BELLEVUE WA, US
Assignee:
AMAZON TECHNOLOGIES, INC. - Seattle WA
International Classification:
H04L 29/08
H04L 12/24
H04L 12/26
Abstract:
A service provider may apply customer-selected or customer-defined auto-scaling policies to a cluster of resources (e.g., virtualized computing resource instances or storage resource instances in a MapReduce cluster). Different policies may be applied to different subsets of cluster resources (e.g., different instance groups containing nodes of different types or having different roles). Each policy may define an expression to be evaluated during execution of a distributed application, a scaling action to take if the expression evaluates true, and an amount by which capacity should be increased or decreased. The expression may be dependent on metrics emitted by the application, cluster, or resource instances by default, metrics defined by the client and emitted by the application, or metrics created through aggregation. Metric collection, aggregation and rules evaluation may be performed by a separate service or by cluster components. An API may support auto-scaling policy definition.
Saurabh D Baji from Bothell, WA, age ~42 Get Report