Senthil Kumar Kumarasamy Mani

VP of Engineering

Bengaluru, Karnataka, India22 yrs 5 mos experience

Highly Stable

Key Highlights

20+ years of R&D experience in IT and data management.
Led multiple successful software solutions saving millions.
Authored over 50 papers and filed 30 patents.

Stackforce AI infers this person is a SaaS expert with a strong focus on AI-driven solutions and data management.

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Skills

Core Skills

MlopsData PipelinesData Ingestion

Other Skills

AnalyticsBusiness analyticsBusiness process managementComputer ScienceCore JavaDeep LearningDevOpsDockerEclipseEnterprise SoftwareExpertise MiningIBM BluemixIT service managementInnovationJava

About

* Passionate about solving business, IT and data management problems by developing innovative solutions. * Strongly believe in application oriented research * Expertise in the following areas of Computer Science and Service Science: Web Services, Text Analysis, Recommender System, Expertise Mining, Business analytics, IT service management, Business process management. Text Processing, Machine Learning and Deep Learning * 20+ years of Research and Development experience. * M.Sc Mathematics and B.E Computer Science (2003) from BITS Pilani * Initiated research projects, led the development of software solutions and successfully deployed solutions that saved millions of dollars at clients and within IBM's operations. * Have hands-on experience building and leading teams across research, product development and engineering Company Awards: * IBM Corporate Award (2014), * IBM Outstanding Technical Achievement Awards (2018, 2017, 2016, 2015, 2014, 2011, 2007) * 10th Invention Plateau Award (2019) - IBM Master Inventor (2018) External : * Program Committee Member of Data Showcase Track in Mining Software Repositories (MSR 2015) Conference * Patents: 30 patents filed, with 16 patents granted * Publications: Authored over 50 papers at refereed and reputed conferences in the areas of Computer Science.

Experience

2 roles

Senior Engineering Manager, Experimentation Platform, Online Infra

Aug 2025 – Present · 7 mos

At LinkedIn, every change to our products and services is rigorously tested through experiments to assess their impact on our members and metrics. These experiments are gradually scaled up to evaluate their effects at each stage before moving forward. We conduct thousands of such experiments daily across all our product offerings resulting in millions of evaluations on the platform, and it's crucial that the results and insights are reliable.
To facilitate this, LinkedIn has established a centralized experimentation platform used by all engineering teams to launch new services or product features with an experimentation flag. This platform allows them to set up experiments, scale them safely, observe results, and gain insights into statistical significance and metric impact. The stability, reliability, and observability of these platforms are paramount. Continuous evolution of our tech stack is essential to support the scale of thousands of experiments. Additionally, as we integrate more AI/ML workloads, our experimentation platform must evolve to meet these new demands. Our goal is to transition from a manual design, execution, and reporting process to a fully automated, AI-assisted experience, to drive increase in experimentation velocity.
We are in the process of forming a new team within the Online Infrastructure organization in LinkedIn India to spearhead this initiative, which is being transferred from the US. We are seeking talented engineers at all levels, from Software Engineers through Staff Engineers to join us in this exciting endeavor.

MLOpsPlatform as a Service (PAAS)People ManagementData IngestionData PipelinesLarge Scale Systems+1

Senior Engineering Manager, Data and AI Platforms

Jul 2021 – Jul 2025 · 4 yrs

[2024 - 2025] As part of the Tracking Team at LinkedIn, we are responsible for instrumenting & collecting accurate data of how LinkedIn users interact with our web & mobile applications. Data scientists use the tracking data to gain business insights, Product Managers use them to understand customer behavior and AI engineers use them to build relevance models to improve our products and services. We are building the next generation Tracking platform to efficiently collect interaction data for our billion plus users cutting across multiple technology stacks (mobile, applications, infrastructure) and generate insights in near real time.
[2021 - 2024] We are one of the core teams behind the Productive Machine Learning (Pro-ML) initiative, bringing a fast end-to-end model and feature goodness to LinkedIn engineering.
Health Assurance: Aims to provide a lightweight, automated, and efficient way to validate & monitor models at inference time as well as catch and raise the visibility of problems as early as possible. It is a key pillar of Productive Machine Learning (Pro-ML)
Model Deployment: Pro-ML model deployment was designed and built to provide a common process and set of tools to allow Pro-ML engineers to easily deploy their models and monitor the health of each model. Pro-ML model deployment provides a suite of tools that enable users to create, track and deploy (or un-deploy) model artifacts to production.
Model Cloud: Serving models for point/batch inference is inarguably operations-heavy and requires teams to build/integrate with the ProML’s serving ecosystem. While this adds complexity at the application layer, it requires non-trivial engineering investment for each team and consequently prevents creating leverage. To address this problem, we are building a managed model-serving solution (Model Cloud) for online inference

MLOpsPlatform as a Service (PAAS)People ManagementData IngestionData PipelinesLarge Scale Systems+1

Flipkart

Senior Engineering Manager

Jan 2020 – Jul 2021 · 1 yr 6 mos · Bengaluru Area, India

Developing a platform for managing the complete lifecycle of AI Model development across data sourcing / management, pre-processing, augmentation, training / experimentation, testing, hosting and monitoring, with data science intelligence baked into the platform.
Developing an Enterprise Knowledge Graph Platform, to enable storing and serving of semantic information about product and addressing selection gap through advanced search and recommendation
Developing an embedding based retrieval platform to store and index multiple embeddings of the product and power solutions like duplication detection, visually similar products and shop the look.

MLOpsPeople Management

Ibm research india

5 roles

Senior Technical Staff Member and Manager

Promoted

Apr 2017 – Dec 2019 · 2 yrs 8 mos

[ 2019 ] - Application Modernization. Developing AI-Based tools and solutions to help modernize monolith applications to be cloud-native. Specifically, focus on recommending micro-services form monolith applications and a modernization assistant which can help in migration of third party dependencies. We are using a combination of natural language processing, machine learning, and program analysis. As part of this work, we have filed 2 patents.
[ 2017 - 2019 ] SE4AI - Software Engineering for AI. There is no structured / principle way of building AI applications. Current SE principles and methodologies have several gaps, in all aspects of requirements, design, implementation, test and deployment when it comes to applying it to building AI applications. We have started investigating this space towards defining process and methodologies and associated tools required to improve the productivity of AI developers, to truly democratize AI.
Toward this goal, we have reduced the barrier to entry for developers by building DARVIZ (internal project name), which allows a developer to visually program a DL model and generate the model code in any of the libraries (TF, Keras, Caffe and Pytorch). This work is now offered as Neural Network Modeller (NNM) in Watson Studio product. As part of this work we have filed 3 patents and 2 publications (ICSE'16 and AAAI 18)
There is a need for both AI developers and Business SMEs to thoroughly test the AI models prior to deploying them in production. Testing AI models is very nascent, with limited process and tooling support and is highly subjective to the skill level of testers. We developed an AI Testing Framework that automates AI Testing by generating and executing test suites that meaningfully represents data in the wild. We have developed techniques for testing the sensitivity and adversarial robustness of AI Models. We filed 2 patents and published 1 paper (InterSpeech 2019, ICSE 2020 & AAAI 2020 - submitted)

MLOpsPlatform as a Service (PAAS)People Management

Senior Software Engineer and Manager

Jul 2015 – Mar 2017 · 1 yr 8 mos

[2015 - 2017] AI Based Automation - The goal of AI based Automation is procedural assistance either as a self–assist solution (assisting the end-users directly) or as agent-assist solution, with the value proposition of increasing productivity and cost savings with efficient problem resolution. In building such a system there exist considerable technical challenges. The knowledge required for training any cognitive system, is not available in well-documented high fidelity format. Practitioners over the years have gained the expertise of resolving issues and this tacit knowledge is not documented in any format. Further, there exist a huge variation across the users in reporting the same issue. The issues can be reported in a very ambiguous ways and requires disambiguation through manual intervention. Thus a AI based general assistance solution needs to be able to handle such linguistic variations, disambiguate the context, and learn and self adapt through its usage.
We built a AI platform IBM Automation with Watson hosting automation specific services, and solutions like Employee / User Assist and Agent Assist on top of this platform. Filed 3 patents and published 3 papers ( IBM R&D Journal'17, ESEC/FSE'17 and AAAI'18)

People Management

Senior Software Engineer

Jul 2012 – Jul 2015 · 3 yrs

[2012 - 2014] Catapult Preventive : Applied machine learning and analytics for deriving insights from mining software repositories like ticketing systems, and code version history - to help stakeholders identify preventive actions to reduce ticket volumes and do effective transitioning in application maintenance space.
Filed 4 Patents and Co-authored 12 papers in peer reviewed conferences

Advisory Software Engineer

Jun 2009 – Jul 2012 · 3 yrs 1 mo

Investigated the requirements management method used in packaged applications (SAP and Oracle) and developed solutions to improve asset reuse and management. Primarily responsible for
Design and Implementation of innovative solutions
Led and Mentored a team of developers
Successfully transfered the research solution to business groups
Filed 4 Patents and Co-authored 11 Papers in peer reviewed conferences

Technical Staff Member

Mar 2006 – Jun 2009 · 3 yrs 3 mos

Worked on realization of Model Driven Approach for Software Development. Developed Eclipse framework based tools for process and UI modeling and automatic realization of the same applying MDD principles. Primarily responsible for
Designing Innovative Solutions through active research and implement them
Successfully transfer research solutions to business groups
Co-authored 4 papers in peer reviewed conferences