diff --git a/README.md b/README.md index 06a801a..c388e62 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,4 @@ -# Check my resume - -[AjayMeena_Resume.pdf](https://github.com/im-ajaymeena/Resume/releases/download/latest/AjayMeena_Resume.pdf) - - -# CI/CD Resume with Docker +# Build Resume CI/CD with Docker and Latex ### Running locally - Steps to run @@ -12,11 +7,18 @@ cd Resume docker compose up ``` -- resume.tex file is being continuously monitored and any change in it triggers latexmk command
to generate updated PDF. This is being done with ```latex\monitor.sh``` - +- resume.tex file is being continuously monitored and any change in it triggers latexmk command
to generate updated PDF. This is being done with ```latex/monitor.sh``` +- output resume would be at ```latex/output/resume.pdf```
### Continuous Deployment - `push` on main branch create release attached with pdf coresponding to latex tex file in main - `push` on develop branch create artifact of pdf coresponding to latex tex file in develop + + +# Check my resume + +[AjayMeena_Resume.pdf](https://github.com/im-ajaymeena/Resume/releases/download/latest/AjayMeena_Resume.pdf) + +AjayMeena_Resume \ No newline at end of file diff --git a/latex/resume.tex b/latex/resume.tex index 2b3cc00..e75d809 100644 --- a/latex/resume.tex +++ b/latex/resume.tex @@ -13,7 +13,7 @@ \usepackage{enumitem} \usepackage{fontspec} % being imported manually at /usr/share/fonts -\setmainfont{Calibri} +\setmainfont[Ligatures=NoCommon]{Calibri} \usepackage{array} \setlength{\textfloatsep}{0.1cm} @@ -21,8 +21,9 @@ {\setlength\itemindent{13pt} \item[\raisebox{.25\height}{\tiny\square}] #1} } +\newfontfamily\roboto{Roboto}[LetterSpace=1.1,Scale=1.1,WordSpace=1.1,FontWeight=400] \titleformat{\section}[block] -{{\vspace{-2pt}}\large\centering}% format +{{\vspace{-2pt}}\large\centering\roboto}% format {}%label {0em}%seperation {\colorsection} % @@ -58,7 +59,7 @@ % \titlespacing{command}{left spacing}{before spacing}{after spacing}[right] \titlespacing{\part}{0pt}{11pt}{-0.5pt}[0pt] \titlespacing{\section}{0pt}{1pt}{-2pt}[0pt] -\titlespacing{\subsection}{0pt}{4pt}{-6.5pt}[0pt] +\titlespacing{\subsection}{0pt}{0pt}{-6.5pt}[0pt] \newcommand\scl{1.05} @@ -72,74 +73,99 @@ %%For Japanese font \usepackage{xeCJK} \setCJKsansfont{IPAGothic} - \begin{document} \renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% - \begin{center} \begin{tabular}{@{}l@{}} -\small\faLinkedinSquare \hspace{1pt} \href{https://www.linkedin.com/in/meena-ajay/}{LinkedIn}\\[1.5pt] -\small \textbf{Birth Date}: 20 Dec, 1996 -\end{tabular}% -\hfill -\begin{varwidth}{\textwidth} -\centering \fontsize{25}{60}\selectfont +\hspace{2}\fontsize{25}{60}\selectfont \firstletter{Ajay Meena} - -\end{varwidth}% +\end{tabular}% \hfill \begin{tabular}{@{}r@{}} -\small \faPhone \hspace{1pt} +81-07085914373 \\[1.5pt] -\small \faEnvelope \hspace{1pt} \href{mailto:ajaymeenajp2020@gmail.com}{ajaymeenajp2020@gmail.com} \\ -[1.5pt] +\small \faEnvelope \hspace{1pt} \href{mailto:ajaymeenajp2020@gmail.com}{ajaymeenajp2020@gmail.com} \\[1.2pt] +\small\faLinkedinSquare \hspace{1pt} \href{https://www.linkedin.com/in/meena-ajay/}{LinkedIn} \hspace{1pt} +\small \faGithub \hspace{1pt} \href{https://github.com/im-ajaymeena}{GitHub} \hspace{1pt} \\ +\small \faPhone \hspace{1pt} +81-07085914373 \\[1.2pt] +% \small \textbf{DOB}: 20 Dec, 1996 \end{tabular}% \end{center} +\section{\scshape{Technical Skills}} + +\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +\renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} +\item \textit{Languages}/\textit{Frameworks}: Python, PyTorch, C++, Flask, Django, Golang, Terraform, SQL, VueJS, JavaScript, TypeScript, Tkinter, Bash, \LaTeX, ONNX, TensorRT, OpenCV +\item \textit{Software}/\textit{Platforms}: Git, GitHub Actions, Docker, Google Cloud Platform, Kubernetes, DVC, Grafana, MATLAB, Conda, Milvus, Cypress, Apache Spark +\end{itemize} + \section{\scshape Work Experience} -\subsection{{\textbf{\scalebox{\scl}{\textbf{Software and AI Engineer}}} | \href{https://www.nablas.com/}{NABLAS, Tokyo} } \hfill\scalebox{0.9}{[Aug'22-Present]}} +\subsection{{\textbf{\scalebox{\scl}{\textbf{AI \& Software Engineer}}} | \href{https://www.nablas.com/}{NABLAS, Tokyo} } \hfill\scalebox{0.9}{[Aug'22-Present]}} \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item \textbf {iLect System: EdTech computing platform for AI engineers} -\SubItem{Designed and implemented new features in collaboration with a team of 4+ developers using Agile/Scrum methodology } -\SubItem{Developed backend APIs with Django REST Framework, frontend with VueJS, TypeScript, Vuex, OpenAPI} -\SubItem{Maintained Kubernetes HPC cluster with CPU/GPU (CUDA) servers running JupyterLab, using Terraform} -\SubItem{Continuous deployment on GCP and testing of frontend/backend using GitHub Actions, and monitoring using Grafana} - -\item \textbf {Deep Learning models as WebApp} -\SubItem{Integrated inference APIs in Flask/Gradio with Frontend, Designed CI/CD strategies (\href[pdfnewwindow=true]{https://demo.nablas.com/visual-inspection}{Anamoly Detection}, \href[pdfnewwindow=true]{https://demo.nablas.com/tts}{Text-to-Speech})} - -\item \textbf {Japanese Traffic Sign Detection | Client Project} -\SubItem{Utilized multi-modal vision/language model for unsupervised localization | Designed Few-shot classification method} -\SubItem{Created Japanese Traffic Sign Classification dataset (35 classes) with the designed query-based object-detection model} - -\item \textbf {Dynamic Modelling of Spur-Gear, Bearing, Shaft to automize fault detection | Client Project} -\SubItem{Formed dynamic model of a mechanical system consisting of gear, bearing, and shaft using second-order ODEs} -\SubItem{Formulated and solved the ODEs governing the motion of the system to obtain simulated vibration response in Python} + + + \item \textbf {Multi-Modal Search Engine for Large Knowledge Base} + \SubItem{Designed microservices-based system architecture | Development of APIs for Vector-Store, Cloud Integration, User \\ authentication, Database Monitoring | Surveyed OSS models for converting media information into latent feature space} + + \item \textbf {OCR for Japanese Text for Esoteric Fonts | AI Consulting} + \SubItem{Trained DBNet for text-localization with 82\% F-score \& SVTR Model for recognition with accuracy 91\% on synthetic-dataset} + \SubItem{Utilized OSS Grammar Checker for validation of extracted text | Client side frontend only Web App with ONNX.js} + + \item \textbf {Fake Image Classification | AI Consulting} + \SubItem{Collected Real and Generated Fake Data from diffusion models with synthesized prompts for training on Custom Domain} + \SubItem{Analysed Artifacts in Frequency Domain of Generated Images from Different diffusion based Text-to-Image models } + \SubItem{Trained Swin Transformer based model achieved 98\% accuracy| Grad-CAM/Shap Value for Interpretability of Predictions} + + \item \textbf {iLect System: EdTech Web Platform} + \SubItem{Collaborating with a team of 4+ developers in Agile/Scrum methodology to design and implement new features} + \SubItem{Reduced database migration time from 5hrs to 20min by utilizing Cloud Run Job \& uinx socket connection to Cloud SQL} + \SubItem{Developing backend APIs with Django REST framework, frontend with VueJS, TypeScript, Vuex, OpenAPI} + \SubItem{Continuous deployment on GCP and testing frontend/backend services using GitHub Actions, and monitoring with Grafana} + \SubItem{Maintenance of Kubernetes HPC cluster with CPU/GPU (CUDA) servers running JupyterLab on GKE, using Terraform} + + \item \textbf {Japanese Traffic Sign Detection | AI Consulting} + \SubItem{Utilized multi-modal vision-language model for object localization | Trained Few-shot classification on custom data} + \SubItem{Created Japanese Traffic Sign Classification dataset (35 classes) with the developed query-based object-localization} + + + + % \item \textbf {\href{https://www.nablas.com/demo}{Deployed Deep Learning models as Web Applications}} + % \item \textbf {Deployed Deep Learning models as Web Applications} + % \SubItem{Integrated inference APIs in Flask/Gradio with frontend in VueJS | Created CI/CD processes for deployment to GCP} + + \item \textbf {No-Code ML Web Platform} + \SubItem{Integrated training/inference APIs with frontend | Used Celery, Redis for asynchronous task processing and task queue} + + + \item \textbf {Physical Modelling for RUL estimation | AI Consulting } + \SubItem{Formed a dynamic model of a mechanical system consisting of gear, bearing, and shaft using second-order ODEs} + \SubItem{Formulated and solved the ODEs governing the motion of the system to obtain simulated vibration response using scipy} \end{itemize} -\subsection{{\textbf{\scalebox{\scl}{\textbf{Assistant Researcher}}} | \href{https://www.hitachi.com/rd/about/location/crl/index.html}{Hitachi R\&D Lab, Tokyo} } \hfill\scalebox{0.9}{[Aug'22-Present]}} +\subsection{{\textbf{\scalebox{\scl}{\textbf{Assistant Researcher}}} | \href{https://www.hitachi.com/rd/about/location/crl/index.html}{Hitachi R\&D Lab, Tokyo} } \hfill\scalebox{0.9}{[Dec'20-July'22]}} \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] % \setbeamertemplate{itemize item}{\raisebox{22ex}{$\bullet$}\hskip10em} \item \textbf {Expanded Person Retrieval system to dark/night environment} -\SubItem{Designed ViT based Infrared \& RGB cross-modal Re-id model using novel patch augmentations with 71\% Rank@1 accuracy} -\SubItem{Superheadead the construction of Cross-Modal Reid dataset, 10 times larger than the size of publicly available benchmark} -\SubItem{Reduced dataset annotation cost from 1890hrs to 200hrs by developing AI models based semi-automatic annotation tool} -\SubItem{Submitted Patent on Multi-Modal tracking system which adaptively utilizes both Infrared \& RGB based visual-features} -\vspace{3pt} -\item \textbf {Aerial View Object Detection for Drones} -\SubItem{Improved detection accuracy on small objects by utilizing sliced-inference with object-detection models (YOLOX, YOLOv5)} -\SubItem{Optimized production-level model with TensoRT, ONNX, OpenVINO. Benchmarked models' memory, GFLOPs, accuracy, FPS} -\vspace{3pt} -\item \textbf {Task-aware Quality Estimation of Peson image} -\SubItem{Designed models and training strategies to measure the informativeness of person images in an unsupervised manner} -\SubItem{Reduced the storage requirements of the tracking algorithm by 10 times by selectively storing informative person-crops} +\SubItem{Designed ViT based Infrared \& RGB cross-modal Re-id model using novel patch augmentations with 71\% rank@1 accuracy} +\SubItem{Completed construction of cross-modal Re-id dataset, 10 times larger than the size of publicly available benchmark} +\SubItem{Reduced dataset annotation cost from 1890hrs to 200hrs by developing AI model based semi-automatic annotation tool} +\SubItem{Submitted a patent on Multi-Modal tracking system which adaptively utilizes both infrared \& RGB based visual-features} + +\item \textbf {Aerial View Object Detection} +\SubItem{Improved detection accuracy on small objects with sliced-inference technique for object-detection models (YOLOX etc.)} +\SubItem{Optimized production-level model with TensoRT, ONNX and OpenVINO. Benchmarked memory, GFLOPs, accuracy, FPS} +\SubItem{Impact: Our team won the contract for building the Tokyo Metropolitan govt. disaster monitoring system worth 77M¥} + +\item \textbf {Task-aware Quality Estimation of Person image} +\SubItem{Designed models and training strategies to measure the informativeness of person image in an unsupervised manner} +\SubItem{Achieved 10x reduction in storage requirements for tracking algorithm by selectively storing informative person-images} \end{itemize} @@ -153,17 +179,17 @@ \subsection{{\textbf{\scalebox{\scl}{\textbf{Assistant Researcher}}} | \href{htt \section{\scshape{Education And Internship}} \subsection{{\textbf{\scalebox{\scl}{Indian Institute of Technology Mumbai (IIT-Mumbai)}} | India } \hfill\scalebox{0.9}{[Jul'15-Jul'20]}} \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item Pursued B.Tech \& M.Tech in Electrical \& Electronics Eng. | Secured {CGPA of 8.38/10} | Master's Thesis in Person-Tracking -\item{Semester Exchange Ko\c{c} University, Turkey | Secured \textbf{SGPA of 9.5/10} | Received \textbf{Erasmus scholarship } of 4,200 EURO } +\item{B.Tech \& M.Tech in Electrical \& Electronics Eng. | Master's Thesis in Person-Tracking | Secured CGPA of 8.38/10} +\item{Semester Exchange Ko\c{c} University, Turkey | Secured SGPA of 9.5/10 | Received Erasmus scholarship of 4,200 EURO} \end{itemize} % \vskip \endpar -\subsection{{\textbf{\scalebox{\scl}{Automated test rig for Electric-Vehicle}} | Greendzine Technologies, Banglore} \hfill\scalebox{0.9}{[May'18-Jul'18]}} +\subsection{{\textbf{\scalebox{\scl}{Automated test rig for Electric-Vehicle}} | Greendzine Technologies, Bangalore} \hfill\scalebox{0.9}{[May'18-Jul'18]}} \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] \renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} -\item Developed the \textbf{design verification plan} for finished goods and \textbf{programmed control flow} for vehicle subsystem-level testing -\item \textbf{Impact:} Proposed design was \textbf{finalized by company CTO, director} and sent for manufacturing +\item Developed the design verification plan for finished goods and programmed control flow for vehicle subsystem-level testing +\item Impact: Proposed design was accepted by the company CTO and director for manufacturing \end{itemize} %######################################################################## @@ -180,23 +206,22 @@ \subsection{{\textbf{\scalebox{\scl}{Automated test rig for Electric-Vehicle}} | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -\section{\scshape{Technical And Organisational Skills}} - -\subsection{{\textbf{\scalebox{\scl}{Programming \& Softwares}}}} -\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} -\item \textit{Languages}/\textit{Libraries}: Python, C++, Flask, Django, Tkinter, PyTorch, Vue.js, TypeScript, HTML, Bash, \LaTeX, SQL, Terraform -\item \textit{Softwares/Platforms}: Git, GitHub Actions, Docker, Google CLoud Platform, Kubernetes, Grafana, MATLAB, Autocad, LabVIEW -\end{itemize} - -\subsection{{\textbf{\scalebox{\scl}{Leadership Skills}}}} -\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} -\item IITB Academic Council Design Nominee: Part of a \textbf{30 member team} dedicated to improving the institute educational system -\item Teaching Assistant | Image Processing: Coordinated in a team in preparing and evaluating semester exams of \textbf{100+ students} +% \section{\scshape{Technical And Organisational Skills}} +% \section{\scshape{Technical Skills}} +% \subsection{{\textbf{\scalebox{\scl}{Programming \& Softwares}}}} +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} +% \item \textit{Languages}/\textit{Framework}: Python, PyTorch, C++, Flask, Django, Terraform, SQL, Vue.js, TypeScript, HTML, Tkinter, Bash, \LaTeX +% \item \textit{Software}/\textit{Platforms}: Git, GitHub Actions, Docker, Google Cloud Platform, Kubernetes, Grafana, MATLAB, Conda +% \end{itemize} -\end{itemize} +% \subsection{{\textbf{\scalebox{\scl}{Leadership Skills}}}} +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \renewcommand{\labelitemi}{\raisebox{0.30ex}{\scalebox{0.4}{$\blacksquare$}}} +% \item IITB Academic Council Design Nominee: Part of a 30-member team dedicated to improving the institute's educational system +% \item Teaching Assistant, Image-Processing: Coordinated with a team in preparing/evaluating semester exams of 100+ students +% \end{itemize} @@ -209,33 +234,42 @@ \subsection{{\textbf{\scalebox{\scl}{Leadership Skills}}}} \section{\scshape{Other Relevant Projects}} - +\vspace{1} % ################################################## -\subsection{\scalebox{\scl}{Real-time Speech Emotion Recognition using CNNs} } \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item Implemented shallow \textbf{CNN} using Keras framework in \textbf{Python} for determining emotional state from \textbf{raw speech} segments + + \item \textbf {Real-time Emotion Recognition using CNNs from raw speech segments} + \item \textbf {Voice Conversion using Conditional WaveNet by conditioning the output on spectrogram of target speaker identity} + \item \textbf {Linear Programming based modelling for parking facility of autonomous vehicles} + \item \textbf {Genetic Algorithm based magnetic characterization for estimation of hysteresis model parameters} + \end{itemize} -\vspace{\otherProjectsSubsectionGap} -\subsection{\scalebox{\scl}{Voice Conversion using Conditional WaveNet} } -\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item Implemented voice conversion model by conditioning the output on \textbf{input spectrogram} and target speaker identity -\end{itemize} +% \subsection{\scalebox{\scl}{\textbf{Real-time Emotion Recognition using CNNs}} } +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \item Implemented light-weight CNN in Python's Keras library for determining emotional state from raw speech segments +% \end{itemize} -\vspace{\otherProjectsSubsectionGap} -\subsection{\scalebox{\scl}{Linear Programming Based Parking Facility} } -\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item Reviewed and implemented advanced \textbf{modeling parking facility} for autonomous vehicles using Linear Programming -\item Implemented \textbf{Benders decomposition algorithm} for optimizing nonlinear model using Mixed-Integer Programming -\end{itemize} +% \vspace{\otherProjectsSubsectionGap} +% \subsection{\scalebox{\scl}{\textbf{Voice Conversion using Conditional WaveNet} }} +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \item Implemented voice conversion model by conditioning the output on input spectrogram and target speaker identity +% \end{itemize} -\vspace{\otherProjectsSubsectionGap} -\subsection{\scalebox{\scl}{Genetic Algorithm-based Magnetic Characterization} } -\begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] -\item Developed a \textbf{genetic algorithm} in visual programming language(\textbf{LabVIEW}) for estimation of hysteresis model parameters -\item \textbf{Impact}: Built setup was first of its kind to calculate the parameters in real-time | Received \textbf{Letter of Recommendation} -\end{itemize} +% \vspace{\otherProjectsSubsectionGap} +% \subsection{\scalebox{\scl}{\textbf{Linear Programming Based Parking Facility} }} +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \item Reviewed and implemented advanced modelling for parking facility of autonomous vehicles using Linear Programming +% \item Implemented Benders decomposition algorithm for optimizing nonlinear model using Mixed-Integer Programming +% \end{itemize} + +% \vspace{\otherProjectsSubsectionGap} +% \subsection{\scalebox{\scl}{\textbf{Genetic Algorithm-based Magnetic Characterization} }} +% \begin{itemize}[topsep=\itembefore,itemsep=\itemgap,partopsep=0pt, parsep=0pt] +% \item Developed a genetic algorithm in visual programming language(LabVIEW) for estimation of hysteresis model parameters +% \item Impact: Built setup was the first of its kind to calculate the parameters in real-time | Received letter of recommendation +% \end{itemize} % \vspace{\otherProjectsSubsectionGap} % \subsection{\scalebox{\scl}{Adaptive Echo Cancellation (AEC)} }