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Vetted Talent

Deepak kumar

Vetted Talent
Passionate and Skilled SDET Engineer with years months of hands-on experience in supporting, automating, and microservice functional, integration testing, API and UI automation and user behaviour analytics using app trackers.
  • Role

    RAG Engineer

  • Years of Experience

    7.83 years

Skillsets

  • Kubeflow
  • fine-tuning
  • Exploratory data analysis
  • EDA
  • DevOps
  • Datastax Cassandra
  • Databricks
  • Apache Airflow
  • MLFlow
  • Kubernetes
  • Geval
  • Docker
  • Deepseek ocr
  • containerisation
  • Computer Vision
  • Azure
  • AWS
  • Toxicity testing
  • Tableau
  • PyTorch Lightning
  • Yolo5
  • Yolo
  • Transformers
  • SQL
  • RoBERTa
  • Retrieval-Augmented Generation
  • REST Assured
  • rag
  • Multi-Agent Systems
  • Pinecone
  • OpenAI
  • Neo4j
  • LLMs
  • Large Language Models
  • Karate
  • Hugging Face
  • Giskard
  • Sagemaker
  • Python
  • Scikit-learn
  • Prompt Engineering
  • pandas
  • NumPy
  • Matplotlib
  • Feature Engineering
  • CUDA
  • AWS Lambda
  • FastAPI
  • OpenCV
  • MLOps
  • LangChain - 2 Years
  • FastAPI
  • CI/CD
  • CI/CD
  • PyTorch - 2 Years
  • Python - 3 Years
  • Deep Learning
  • Model reliability
  • model optimization
  • Model evaluation
  • Model deployment
  • ML Pipeline
  • LangGraph
  • Hallucination testing
  • foundation models
  • Java - 5 Years
  • Bias testing
  • benchmarking
  • Artificial Intelligence
  • AI Agents
  • Machine Learning
  • LangChain
  • AWS Lambda
  • Generative AI

Vetted For

3Skills
  • Roles & Skills
  • Results
  • Details
  • icon-skill_image
    Senior QA AnalystAI Screening
  • 50%
    icon-arrow-down
  • Skills assessed :Quality Analyst, Automation Testing, Quality Assurance
  • Score: 50/100

Professional Summary

7.83Years
  • May, 2025 - Present1 yr

    AI ML Engineer

    Sony India Software Centre
  • Jun, 2024 - Jun, 20251 yr

    Generative AI Engineer

    Eli Lilly and Company
  • Feb, 2023 - Jun, 20241 yr 4 months

    Artificial Intelligence , SDET

    CRED
  • Jun, 2018 - Apr, 20212 yr 10 months

    SDET

    Coviam Technologies
  • Apr, 2021 - Dec, 2021 8 months

    SDET-1

    BYJU'S
  • Dec, 2021 - Jan, 20231 yr 1 month

    Software Development Engineer In Test

    Navi

Applications & Tools Known

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    Appium

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    Selenium

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    REST API

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    REST Assured

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    Deep Learning

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    Java

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    Python

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    pytest

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    testng

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    Cucumber

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    Rest Assured

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    Appium

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    Espresso

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    Cypress

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    Jmeter

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    Testng

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    Cucumber

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    Jenkins

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    Github

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    Bedrock

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    Llama

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    Jira

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    MongoDB

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    Robo3T

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    Spring Boot

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    Android Studio

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    PostgreSQL

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    LLM

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    CNN

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    RNN

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    YOLO

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    Postman

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    Hadoop

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    Apache Spark

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    Tableau

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    OpenCV

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    Agile Methodologies

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    Firebase

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    Crashlytics

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    Springboot

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    Grafana

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    Kibana

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    Kafka

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    Kubernetes

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    Report Portal

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    Testing

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    Maven

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    AWS

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    Redis

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    AWS Sagemaker

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    AWS Bedrock

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    Tensorflow

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    Pinecone

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    FastAPI

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    LLM

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    CNN

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    FAISS

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    Tableau

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    Maven

Work History

7.83Years

AI ML Engineer

Sony India Software Centre
May, 2025 - Present1 yr
    Established scalable MLOps framework using Kubeflow, Docker, and Kubernetes, enabling repeatable training and production deployment with automated approvals and monitoring. Designed, developed and deployed machine learning models for Sony playstation for the business requirements and worked on the playstation recommendation system store. Created end to end pipeline for models development, hyperparameter tuning deployment and monitoring of the ML models using MLFlow and KubeFlow, Automating preprocessing tasks using Apache Airflow for seamless job orchestration. Fine Tuned Claude Sonnet 4.5 models using q-LORA with internal marketing data to increase the efficiency of QnA system by 7%. Integrated MLflow tracking and CI/CD gating, enforcing reproducibility and model version control; reduced development-to-production cycle time by 70%. Designed Agentic services using A2A (GOOGLES AGENT TO AGENT PROTOCOL Protocol) and MCP (Model Context Protocol), enabling scalable multi-agent communication across gaming platforms considering best LLMOPs practices. Developed Tone Guard, a GenAI-powered chatbot filter transforming abusive inputs into respectful responses, reducing toxicity reports by 40%. Implemented an evaluation pipeline for multi-agent systems using CREW, improving reliability of orchestration and reducing error propagation by 25%. Created classical ML models for PlayStation security and network traffic analysis, detecting anomalies and fraudulent activity with 92% accuracy. Used knowledge graph to increase the accuracy of the dashboard query system where user can query from dashboards which has table charts and maps of the movies produced by Sony Pictures. SQL agents for multi databases query system using knowledge graphs and agentic training on neo4j databases. Evaluated the accuracy to 95% and added human in loop to confirm. Developed machine learning models to production for playstation stores services for recommendation, insights and business requirements to production and monitored the performance and drifts. Used MLFlow and databricks and sagemaker for scalable and versioning of the model infrastructure. Did POC on voice agents using Whisper, Langchain and Langgraph to convert a human voice to a characters voice like Ironman and Spiderman.

Generative AI Engineer

Eli Lilly and Company
Jun, 2024 - Jun, 20251 yr
    Managing end-to-end model lifecycle, steering problem scoping, data preparation, experimentation, deployment and scaling. Developing and deploying Gen AI applications and multi-level AI agents using OpenAI, Claude and AWS Bedrock Agents, accelerating data access and querying. FineTuned GPT 3.5 to increase the accuracy of the Scientific E Auth system (QnA) system by 11%. Implemented ROUGE based and LLM as judge based accuracy parameter to evaluate generation content. Creating a Q&A system using RAG, Pinecone and Roberta, facilitating query and summarization of drug release documents. Exploring Hugging Face LLMs, recommending appropriate models for query management. Improved retrieval speed by 20% using query translation (e.g., RAG Fusion), indexing, re-ranking, and caching techniques. Conducting systematic experimentation and hyperparameter optimization, enhancing model performance. Optimizing the foundation models using LangChain, enhancing response quality. Conducting exploratory data analysis (EDA), feature engineering and statistical modelling to drive actionable insights for ML systems. Led POC and migrated agents to MCP and A2A methods to streamline LLM data access. Building Cortex Service to enable prompt engineering with LLMs via FastAPI. Securing company data using Agentic AI and supporting AI agents with LangGraph-based orchestration. Designing evaluation pipelines for GenAI summarization with GEval and Giskard, for monitoring LLM performance. Maintaining production-grade DataStax Cassandra DB, optimizing RAG system performance and data retrieval speeds. Incorporating MLOps/LLMOps pipelines for Gen AI projects, streamlining project workflow and delivery. Validating LLMs for hallucinations, biases and toxicity with GLUE, Giskard and RAGAS, improving model safety and reliability. Customizing test sets and metrics to validate factual accuracy, ethical alignment, and output reliability across diverse prompts.

Artificial Intelligence , SDET

CRED
Feb, 2023 - Jun, 20241 yr 4 months
    Developed evaluation matrices for AI-based lending approval products, testing and improving product performance. Optimized ChromaDb and PineCone integration, driving LLM retrieval systems to reduce query latency and ensure real-time response. Built test cases and Gen AI evaluation frameworks using RAGAS, GEVal and Giskard, automating and improving testing throughput by 40%. Created chatbots for internal document queries, for accessing data using natural language by the OPS team. Steered ML pipeline development and CI/CD integration, enhancing workflow efficiency. Optimized LLMs for enterprise and customer-facing applications, reducing hallucination and enhancing performance. Engineered LLM fine-tuning workflows using Hugging Face, PyTorch Lightning and Sagemaker, accelerating training and deployment. Implemented computer vision models with OpenCV, Tensorflow, Yolo5, AWS Sagemaker and AWS Lambda to identify and report warehouse product defects. Deployed AI models on AWS bedrock, aligning cross-functional business objectives with technical feasibility.

Software Development Engineer In Test

Navi
Dec, 2021 - Jan, 20231 yr 1 month
    Developed machine learning models and AI chatbots using natural language processing (NLP) methods and data science library like pandas, scikit learn and tensorflow. Trained, evaluated, and optimized models using machine learning and statistical techniques. Analyzed large-scale datasets, identifying discrepancies and improving model accuracy and performance. Evaluated and improved ML model and chatbot performance, providing insightful feedback. Created AI chatbot test automation framework, enhancing test efficiency and defect detection. Automated API and UI testing, optimizing software quality, performance and release time.

SDET-1

BYJU'S
Apr, 2021 - Dec, 2021 8 months
    Managed software testing lifecycle (STLC) and quality processes, delivering Web and mobile applications. Created test automation frameworks and scripts using Selenium, Rest Assured and open-source tools, driving efficient releases. Simulated test cases, tracking test holes and refining test plans against code coverage data. Automated regression suite execution with Jenkins, reducing development cycle and release time. Enhanced system efficiency and reliability with automated data tracking from mobile and web applications.

SDET

Coviam Technologies
Jun, 2018 - Apr, 20212 yr 10 months
    Conducted manual and automation testing of AI chatbots, identifying and reporting bugs to the development team. Curtailed manual testing efforts by 40%, implementing automated test methods and tools. Tracked, analyzed and documented test issues, risks and solutions, facilitating product progress and on-time release. Facilitated continuous product improvement via thorough code review and constructive feedback. Mentored junior engineers on industry best practices, enhancing productivity and software quality.

Major Projects

4Projects

OPS PORTAL

NAVI TECHNOLOGIES
Mar, 2022 - Dec, 20231 yr 9 months

    Worked on the qa for ops portal and led the automation team for regression of services

Mobile APi

Quinnbay Technologies/Coviam Technologies
Jun, 2018 - Jan, 20234 yr 7 months

    Worked in a Mobile team and its backend service

Gamification

Coviam Technologies
Dec, 2019 - Apr, 20211 yr 4 months

    Woked on qa and automation for game service from scratc.

ecting mobile API service and app migration to di erent rest API service (cid:123)

Education

  • BTech: Electronics & Communication Engineering

    Maharaja Agrasen Institute of Technology

Certifications

  • Deep Learning Specialzation

    Coursera

AI-interview Questions & Answers

I work at Grid. Currently, I'm working at Grid. I have total 5 years of experience in both backend as well as mobile as well as web testing. I started my career at Kinvey Technology, formerly known as Covium Technology. So I started as a manual tester there, and then gradually moved to software development engineering test. So, basically, my day-to-day job over there was testing both mobile as well as backend APIs. I used to handle some of the backend ports over there. Then I moved to Baidu's technology where I was a mobile as well as web automation engineer over there, and I learned mobile and web automation over there. Then gradually, after that, I moved to Navi Technologies in December 2021, and I worked as a lead SDET over there. So after February 2023, I moved to Grid technology, which I'm currently working here. So, basically, I'm currently leading a port, which is a win port. This is a port for playing games inside the CRED app, and we give rewards to the users who win inside the game. So, basically, my day-to-day job is handling port features, all the features that are coming in the sprint, as well as automating all those scenarios for the mobile app.

We have used Jira technology. We used to create tickets, as well as Confluence. We used to create tickets, and those tickets used to go to different development cycles, like different site process cycles, development to the product manager, and then finally to QA. We used to close those tickets.

Black box testing is generally when you don't know the context of the code, like if you know what the code is, and then it is called white box testing. And so, basically, if you are doing unit testing, it is a white box testing because when you need testing, you are looking at what the code is inside the development code. It is done generally inside the development code, while white box testing. Coming to the black box testing, we consider that regression testing, smoke testing, and sanity testing. These kinds of testing are like black box testing because we don't know what the code is inside. So, these are black box testing.

So, basically, I have worked with product managers as well as development teams. So, basically, we used to grasp all the testing scenarios, what output of the product is, and how it's gonna influence the customer as well as our business values. Then, you would write the test cases, which would be reviewed by developers and product managers. After that, we used to constantly check and give some improvements in the product also, or know the product's architecture, how it can be faster, how it can be more better, when it comes to users. You had a perfect perspective. Sometimes you got missed by the product managers and developers. So, we used to add some values over there. So, basically, I can say that I have mostly worked with product managers' teams and as well as developer teams, mostly with them, and used to work on the whole release cycle as well as after the release has happened, we used to work.

The industry standards. I'm not aware of this either. What are they?

Script language, I used Python before this. We used BDD for making the framework inside for Python and we used Selenium and all in Python only. When it comes to script language, I am also well aware in Java. So we also use TestNG as well as Java, and Cucumber, and Selenium, Nappy, or TestNG. So, Python, when it comes to script language, I use Python mostly.

If all the p values, p 1 and p 2 are covered, and the user is not blocked, then when it comes to the product, the user is able to use the product. We can then say that our product meets the regulatory compliance standard. So, basically, in our current company, if the UAT test scenarios, as well as some p values, meet the requirements, then we are okay with their compliance standards.

It depends upon what type of framework we are developing. If it is a back-end only, we will approach it by considering how many services are being called inside this framework and how many teams are working collaboratively. These two things are the most basic things, and how many API calls, services, and database access are needed will be evaluated. We will also be evaluating truth cases, like if something is coming from the response, how is it expected to be. So, basically, we will try to model and modularize all these things inside our back-end test framework. When it comes to UI, let's say mobile, we will develop a framework where both mobile and UI can be handled inside the same framework. We can create an interface which can be implemented by both mobile and web, and all the scenarios are the same in mobile and web. However, web should have different methods when it comes to implementation. So, basically, all the test cases will be the same, but the implementations will be different for both things. We try to develop a framework that is

Security. So, basically, in terms of QA, the most important thing is learning what's happening around the globe in terms of QA. There are many such things in AI also, we need a lot of QA. And now there has been a very good notion coming over here for AI Testing. So for model testing, what are the models being developed? So, basically, the plans would be we learn and develop because this is a very new field when it comes to QA. Technologically, it is growing very rapidly. It's a revolution inside the current way of QA. You can see it. So, basically, we should learn and grow according to the current industry trends. So, basically, we should try to implement new things coming in the market and see what works.

So, basically, the role of QA is the, like, one of the most important roles inside the development and the process of improvement. It is because mostly it ends the development process with the QA, and QA are the, like, owners of the product after it is developed and advanced to the users. So, basically, we can say that we are the holders. The QAs in India are the holders of the product. It should not break after that. It generally starts with the business team and product managers, and it ends with the QA only. So, we, as QAs, have a very important role in the product development cycle, and notice the role of QA in the engineering process improvement. And when it comes to process improvement, QA has a very different, very good role because QA interacts from the beginning of the product development, like, when the product is being made and all the portfolios for the product and new designs are being made. Then only the QA process can try to be a mediator between product managers and developers. So that time as well as the efficiency of the process can be improved. So, basically, Qwik handles all the process cycle, and it can mitigate a lot of issues if, like, it can increase a lot of efficiency if you can see, it can mitigate a lot of issues.

Yes, I have worked in load testing for a service which is a gamification service. So, basically, we used to have a lot of events called 10-10, 11-11, 12-12 in my first company. So, basically, what we serviced was events. We used to create events. So during those events, like, even 10,000 users would be simultaneously playing the games. We used to check the number of ports that needed to be there for the system. It needs to be installed, and any additional parallel ports need to be installed for the system to handle those numbers of current users. So, basically, what we did is, we would hit create in our QA environment. What we did is, we checked the number of ports used and checked the performance of the port when we checked the eligibility of the user. You would then play the game, and all the processes were there, making a replay of the actual production environment that existed. In case when it was in case, when the production was going on.

I have worked on GitHub workflows by creating automated triggering of automation scripts. I have worked on Jenkins as well. I have also worked on GoCD. Although I don't have much experience in GoCD, I have used already configured GoCD pipelines. I have configured GitHub workflows myself only. And I have worked on Jenkins with Microsoft. So, I have created many pipelines in Jenkins. I have also worked on AWS.