Pratheek Unni

Software Engineer

Bengaluru, Karnataka, India2 yrs 10 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Strong foundation in Machine Learning and Deep Learning.
Experience in developing and optimizing algorithms for real-world applications.
Encourages open source contributions and community involvement.

Stackforce AI infers this person is a Machine Learning and DevOps specialist with a focus on AI-driven solutions.

Contact

Skills

Core Skills

KubernetesMicroservicesJavaSpring BootAnsibleTerraformMachine LearningDeep Learning

Other Skills

ArgoCDBashC++CommunicationData StructuresGitOpsHelmJenkinsMicrosoft AzureObject-Oriented Programming (OOP)PlatformPython (Programming Language)Reinforcement LearningTeam Leadership

About

A quick learner having a perpetual thirst for knowledge. There isn't any favourite particular computer science concept of mine, but rather would love to explore as much as possible. Actively encouraging students and individuals to do open source contributions and get involved in such communities. Building things at day and breaking it at night.

Experience

Whatfix

4 roles

SDE-2 (E4)

Promoted

Sep 2025 – Present · 6 mos · Bengaluru, Karnataka, India

SDE-1 (E3)

May 2023 – Sep 2025 · 2 yrs 4 mos · Bengaluru, Karnataka, India

Platform Engineering

KubernetesMicroservicesPlatformJavaSpring BootArgoCD+3

Software Engineer Intern

Mar 2023 – May 2023 · 2 mos · Bengaluru, Karnataka, India

JavaPython (Programming Language)Spring Boot

DevOps Engineer Intern

May 2022 – Mar 2023 · 10 mos · Bengaluru, Karnataka, India

AnsibleTerraformJenkinsMicrosoft Azure

Inria

Research Associate Consultant (Remote)

Apr 2022 – Dec 2024 · 2 yrs 8 mos · Paris, France · Remote

Worked with the Applied Machine Learning department to research, consult, develop and implement ML solutions to solve real world problems.
Developed Reinforcement Learning algorithm for generating SCHC rules in a closed IOT network.
Collaborated with Smart Europe to create a smart water tap for tracking usage and leakage detection.
Optimized CNN for edge devices through quantization and pointwise, depthwise layer separation. This increased the efficiency of image analysis by roughly 48% (compute time + energy)
Proposed, designed and implemented a hybrid autonomous supervised classifier with no human intervention. LLM as the labeller and Classifier agent as the component.

Machine LearningReinforcement LearningDeep Learning