PLN Rahul

Director of Engineering

Gurgaon, Haryana, India7 yrs 9 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Led product engineering for innovative messaging solutions.
Expert in backend development with Java and Spring.
Passionate about mentoring and empowering new talent.

Stackforce AI infers this person is a Backend-focused engineer in the SaaS industry with expertise in product engineering.

Contact

Skills

Core Skills

JavaSpring FrameworkMachine Learning

Other Skills

Java 8MongoDBElasticsearchRedisGraphQLDockerKubernetesApache KafkaGroovyService MeshInterviewingGradleHelm ChartsApache SparkPython

About

As Director, Product Engineering at Sprinklr, I lead a team of product engineers who work on various features and system-wide optimisations as part of the CARE team. Some of the key functionalities that I have built and owned over the years include Cobrowsing, Guided Workflow, CARE reporting, and modern messaging channels such as Google Business Messaging, WhatsApp Business, and Facebook Messenger. I also have regularly contributed to improving the performance and scalability of the system. I am a keen learner who loves learning new topics and technologies that are used in production level. I also have a strong academic background, having graduated with a Bachelor of Technology degree in Electrical & Electronics Engineering from the National Institute of Technology Warangal. I am passionate about creating and delivering high-quality products and solutions that solve real-world problems and enhance customer satisfaction. I am always willing to learn with no constraints of the technology being used, and I enjoy working with diverse and talented teams.

Experience

7 yrs 9 mos

Total Experience

2 yrs 7 mos

Average Tenure

7 yrs

Current Experience

Sprinklr

4 roles

Director, Product Engineering

Promoted

Jun 2024 – Present · 2 yrs

Associate Director, Product Engineering

Promoted

Apr 2023 – Jun 2024 · 1 yr 2 mos

Worked as a part of CARE team during this period.
1) Owned & led the backend part of Guided Workflow team.
2) Deeply involved in making CARE reporting more resilient & usable for customers.
3) Involved in building Agent-initiated Cobrowsing & general maintenance and resiliency of Cobrowsing as a feature.
4) Led backend side for building Agent Screen Recording for QM purposes, from scratch.
5) Involved in other miscellaneous customer onboarding features as a developer as well as a leader.
6) Led a new team of fresh graduates and empowered them to learn new modules/technologies, helping them to get self sufficient.

Java 8Spring FrameworkMongoDBElasticsearchRedisGraphQL+5

Lead Product Engineer

Apr 2022 – Apr 2023 · 1 yr

Worked as a part of CARE team.
1) Built & owned many new functionalities for Cobrowsing.
2) Built new functionalities for Guided Workflow & started owning the feature end-to-end.
3) Owned the "humungous" CARE reporting end-to-end & acted as the single point of contact for it across the organisation.
4) Mentored newer folks to learn & understand the codebase, tech-stack, best practices & much more!

Service MeshGroovyInterviewingKubernetesGradleGraphQL+10

Product Engineer

Jun 2019 – Apr 2022 · 2 yrs 10 mos

Worked as a part of CARE team. Have been responsible for owning as well as contributing to various features & system wide optimisations including :-
1) Smart Clustering for customer care cases, surveys.
2) Improved performance of case updates in system, by optimising update process resulting in sizeable reduction in Mongo oplog.
3) Contributed to several CARE reporting use cases, for easy data visualisation by customers.
4) Owned several Modern Messaging Channels including Google Business Messaging, WhatsApp Business, Facebook Messenger etc, end to end, within Sprinklr workflow.

InterviewingKubernetesGradleGraphQLElasticsearchDocker+7

Aufenbach

Intern

Oct 2018 – Feb 2019 · 4 mos

Wipro limited

Intern

May 2018 – Jul 2018 · 2 mos · Hyderabad Area, India

Worked on the project titled "Use of Emerging Technologies such as Machine Learning and Artificial Intelligence to proactively detect and prevent frauds in BFSI".
Initially, a thorough research was done to understand the basics of Machine Learning and Artificial Intelligence. This was followed by a study of the various technologies, that are currently available in the domain of AI and ML; including both Open Source and Commercial Technologies. This was followed by model development, in incremental steps. Initially, the neural network approach was adopted which gave extremely high accuracies(≈99%) on the training set and relatively lower accuracies on the test set, which was primarily due to a small amount of data available. Then, the Random Forest Approach was also considered. This gave better results than Neural Networks; as the problem of overfitting was successfully handled. It gave similar accuracies for both the training and test datasets.
Technologies and frameworks used:-Python, Tensorflow, Pandas, Scikit-learn

PythonTensorflowPandasScikit-learnMachine Learning

Dr narla tata rao thermal power station

Internship Trainee

May 2017 – May 2017 · 0 mo · Vijayawada, India

Learnt about the overall working of a thermal power plant, by visiting the plant and learning about various parts of the power plant from Engineers working at the plant.
Was a part of a 15-day training programme conducted by Dr Narla Tata Rao Thermal Power Station(formerly Vijayawada Thermal Power Station). On each day, classroom sessions were held to explain the theoretical aspects related to various parts of a thermal power plant; with different topics being taught by experts in the respective fields. This was followed by actual plant visits, that involved the explanation of different places, already covered in the theory classes held before. Experienced and skilled engineers led teams of students and took turns to explain the different parts of the power plant.
The training program culminated with the conduction of a qualification test based on all the topics and handing over of certificates.

Internshala

Student Partner

Mar 2017 – Aug 2017 · 5 mos · NIT Warangal

The Internshala Student Partner program was primarily about social media marketing. The ISPs were asked to proactively advertise the various products by Internshala, like online courses. Based on the effectiveness of the respective ISP, points were awarded based on a pre-set scale.
Was the 51st best performer across the country and the best performer in my zone. Featured in the list of top performing ISPs for the month of April 2017, on Internshala website. Also won a free course on Internshala Trainings, for getting a high number of referrals.