Anil Kumar Saw

• Project : Gen AI powered Creative Content Management
• Account : Intuit
• Gen AI powered content generation across all marketing channels that is usable, is personalized and meet the intuit standards.
• Tech Stack: Backend: JKD 21, Spring Boot, Spring Security, Spring Rest, Spring Data JPA, Hibernate, Spring Cloud, DynamoDB, Event driven framework: Apache Kafka 3.0, Others: Git for Version control, Postman for API testing, AWS, docker for containerization, Azure Kubernetes services for container orchestration and Splunk as a logging and monitoring tool, Cloud: AWS, Jenkins, Cache: Redis, UI: Angular 17, Typescript.
• Functionalities: -
   With technological advancement like GPT content creation has become more accessible and efficient.
  This has significantly reduced the time and cost associated with content creation making it accessible to
  business of all sizes, as the technology continues to evolve content creation has become even more
  sophisticated, with a greater emphasis on personalization and customization to meet the demand of
  increasingly discerning the audiences. the content generation asks are focused on these following 6
  pillars.
   Content Storage
   Content Quality (Usable vs non-Usable Content)
   Benchmarking and LLM model’s Evaluation
   Model feedback (Human and Reinforcement learning

Responsibilities: -
 Involve in major all development from scratch of the project.
 Writing the core business API for the Project
 Involve in writing the integration and unit test case using wire Mock, Mockito and Junit library.
 Involve in fixing bug for other Project like OINP (One Intuit Notification Platform) .

• Project : ALMP
• ALMP is food delivery platform and a delivery partner (how to create the order is out of scope for ALMP). Americana operates food products throughout Middle East and North African regions.
• Module: Order, Store, driver, payment, System module, driver on demand feature, Store pooling, Driver mobile App, Chat service, GIS, Notification, Kafka, Report, Weather Service.
• Tech Stack: Backend: JKD1.17, Spring Boot, Spring Security, Spring Rest, Spring Data JPA, Hibernate, Spring Cloud, Node.js, PostgreSQL, MongoDB as NoSQL Database and Web Socket, Event driven framework: Apache Kafka 3.0, Others: Git for Version control, Postman for API testing, AWS, and Azure Cloud for deployment, docker for containerization, Azure Kubernetes services for container orchestration and New Relic as a logging and monitoring tool, Cloud: Microsoft Azure and AWS, Jenkins, Cache: Redis, UI: Angular 15, Typescript.
• Functionalities: -
   ALMP is food delivery platform like Swiggy or Zomato and are the delivery partner
   how to create the order is out of scope for ALMP.
   ALMP receive the order from third party integration called IPAAS
   Basically, ALMP receive three types of order events from IPAAS
  1. order creation 2. order update 3. cancel order
   ALMP is responsible to manage order flow from order creation to delivery of Order
   order is created either at SDM or BSP and sent back to integration layer called IPAAS, and IPAAS receive
  the order and sent it to azure function which is exposed by ALMP. IPAAS waits every time for 2
  minutes and accumulates this order in batch and send back it to azure function.
   azure function processes the order as per ALMP system. azure function generates the batch-correlation
  which is common for all the order in that batch assign it to every individual order which identifies the
  order belong to this batch.
   apart from this, orderCorrelationId is also generated which is unique to every individual order which
  uniquely identities this order in ALMP.
   Now azure function sends it to Kafka topic and sends the acknowledgement back to IPAAS.
   if IPAAS does not receive the acknowledgment within 30 seconds they will resend the order
   other services like MS-Order-Service and Kafka-order-processor services are listening to this topic and
  process the data.
   If orderStatusName in payload is unassigned then MS-service will check whether it is duplicate or not, if
  not then it will create the order otherwise it will send the acknowledgement as duplicate order to Kafka
  topic.
   Kafka order processor service will check whether delivery Lat-long is 0 or not,
   if yes then it will generate delivery Lat long based on delivery address
   then it will calculate ETA based on delivery Lat-long and store Lat-long using google map API
   then it will post the data delivery Lat-long, estimated distance and estimated time and store Lat-long in
  Kafka topic.
   then MS-Order-service will consume the data and update it to MS Db.
   in the same way update and cancelled event is trigger based on order status name
   Order Status Workflow:
  Order Status -> unassigned -> assigned
  assigned -> seen
  seen -> scanned
  scanned -> order picked up
  order picked up -> delivered
   Driver Status Workflow
  driver status - >available
  available -> scanned
  (scanned) -> waiting for picked up
  waiting for picked up -> out for delivery
  out for delivery -> returning
   So far, we have onboarded approx. 15 thousand drivers, 3 thousand stores and 30 thousand users with
  different role and our system scale out with the capacity of 2 lakh orders per hour during peak time
  (Eid, Ramadan)

Responsibilities: -
 Involve in major all development from scratch of the project.
 directly Interacted with the principal architect and manager to implement all the major functionalities of the
application like Kafka integration at MS side, order flow with different services like order, Kafka aggregator
processor, driver services, driver service, store service and other services
 login-logout, SSO, security, database design for the application,
 Writing the core business API for the different micro services like store, order, payment, driver, Kafka
aggregator, Payment,
 Involve with Client call to understand the business requirement.

• Project: SMRC PROJECT PERFORMANCE ANALYTICS
• Client: SMRC (a part of Samvardhana Motherson Group)
• To make SMRC's Project Tracking Process more efficient by leveraging Strong Digital Platform & Analytics.
• Tech Stack: -
   Backend: JKD1.8, Spring Boot, Spring Security, Spring Rest, Spring Data JPA, Hibernate, Spring Cloud, MySQL
   Frontend: Angular 10, Typescript, Bootstrap4, CSS, Angular material
   Others: Git for Version control, Postman for API testing, Azure Cloud for deployment, docker for
  containerization, Azure Kubernetes services for container orchestration.
   Cloud: Microsoft Azure, AWS

Functionalities: -
 GPOR: used to track financials and overall status of all the projects from the time of winning the project
to post-launch stage which is 3-6 months after start of production.
 ICF: to track Customer Contributions (Revenues) i.e., payment done by Customers for financing D&E,
CAPEX, Launch Cost etc. For the Projects. This process is implemented only in French & German BUs
 PTR: to track Tooling related Cashflows, Margins & POCM Margins of each project during the life cycle of
Projects.
 R&D: to track Direct & Indirect Revenues & Expenses of Technical Centers for all BUs and allocation of
Costs to other operating Plants by CSA Mechanism:

Responsibilities: -
 Involve in major all development from scratch of the project.
 directly Interacted with the senior architect and tech lead to implement all the major functionalities of the
application like microservices based architecture,
 login-logout, SSO, security, database design for the application,
 Writing the core business API for the services MDM, Gate1, GPOR, PTR, ICF and R&D Process
 Work on both backend and front
 Involve with Client call to understand the business requirement.
 Sometime make the deployment on azure also

• Company Overview: Core produced based US development Company
• This solution solves the common problem of authentication and authorization for various applications across the organization.
• Core produced based US development Company
• Project: Role Based SSO & Access Control
• Client: US Bank, Visa, Bank of America, ICICI, SBI and 30 others.
• Tech Stack: Java, Spring-(Core, Security, MVC 4.0, Boot 2.0), Jersey REST, Eclipse link, JavaScript MVC, Angular 6.0.