Lakshay Tokas
Data AnalystAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Senior Software Engineer - Deep Learning @ Intel Corporation | Computer Engineering
Intel Corporation empowers cutting-edge deep learning innovation through optimization of frameworks like Pytorch, Tensorflow and Onnxruntime for next-generation platform xPUs. As a software engineer with over nine years of experience, contributions focus on code development, performance analysis, and framework optimization for high-performance computing (HPC) applications. Collaborating with teams, we enable AI frameworks and platforms to meet demanding computational needs. Within the Platform Engineering, and Client Computing Groups, efforts center on advancing xPU capabilities of Intel's next generation platforms. By bridging deep learning frameworks with hardware efficiency, work supports scalable solutions for ISVs and CSPs. Passion for enabling AI technologies aligns with driving impactful, performance-driven advancements in the field.
Georgia Institute of Technology
Master’s Degree, Computer Engineering
January 1, 2014 – January 1, 2016
Manav Rachna College Of Engineering
Bachelor of Technology (B.Tech.)
January 1, 2010 – January 1, 2014
Intel Corporation
Software Engineer
June 1, 2016 – Present
Chandler, Arizona
Intel Corporation
Software Engineer - Deep Learning
June 1, 2016 – Present
Chandler, Arizona
Intel Corporation
Software Enabling Intern
August 1, 2015 – December 1, 2015
Greater Phoenix Area
Distributed 2-D Discrete Fourier Transform(C, MPI)
August 1, 2014 – December 1, 2014
The project aims at studying distributed computing by implementing a 2D-DFT of an input image using 16 CPUs. The computation of the 256x256 pixel input image is parallelized using MPI. The correctness of the computation is established by calculating the inverse of the transformed data, and comparing the resultant image with the original.
Implementation of C++ vectors in C using templates (C)
August 1, 2014 – December 1, 2014
The project aimed at studying object-oriented and memory management concepts in C++ by implementing the Vector container and Vector Iterator in C. Wrote functions like PushBack, PushFront, Resize etc, which could be used to push at front, push at the end, change the size of the dynamic array etc.
Encryption/Decryption using the RSA algorithm(C, GNU Library)
August 1, 2014 – December 1, 2014
Used the famous RSA(Rivest-Shamir-Adelman) algorithm to encrypt a random message using a public/private key pair, using the GNU MP library. The encryption was done using the public key, whereas the decryption was done using the private key, and verified that the decrypted message matched the original message for a key length ranging between 64 to 1024 bits.
Cache Coherence Simulator(C++)
August 1, 2014 – December 1, 2014
This project involved the implementation of Cache Coherence protocols such as MSI, MESI, MOSI, MOESI and MOESIF for 4-core, 8-core and 16-core Multiprocessor Systems. Worked on an existing framework to deduce the most optimum protocol for different experimental traces.
Mandlebrot Set(C++, OpenGL)
August 1, 2014 – December 1, 2014
Computed the Mandlebrot Set using c++ and Nvidia's CUDA API. Calculation of pixel values was optimized by the parallel thread model. Later, OpenGL API was used to display the Mandlebrot set.
Simulator for Out-of-Order Superscalar processor using the Tomasulo Algorithm (C++)
August 1, 2014 – December 1, 2014
Implemented a Superscalar Processor with Out-of-Order execution using Tomasulo Dynamic Instruction Scheduling algorithm. The project involved simulating all pipeline stages of the processor. Experimental traces were used to obtain the optimum processor configuration for the best IPC (Instructions Per Cycle) value.
Cultural Fit Analysis
The candidate's experience is heavily focused on low-level software optimization, deep learning framework enablement, and system architecture, primarily within Intel Corporation. While these are highly technical roles, the target role of 'Data Analyst' requires a different skill set, typically involving data manipulation, statistical analysis, visualization, and business intelligence tools (e.g., Python, R, SQL, Tableau, Power BI). The candidate's projects are also very hardware/system-centric (DFT, C++ vectors, RSA, Cache Coherence, Mandlebrot Set, Tomasulo Algorithm). There is a significant mismatch between the candidate's demonstrated skills and the typical requirements for a Data Analyst role, suggesting a low cultural fit for this specific target role.
Soft Skills & Operational Fit
The candidate's project descriptions and work experience highlight a strong problem-solving aptitude and a focus on optimization and performance. Collaboration is evident through working with HandBrake and Intel Media SDK teams. However, specific soft skills like leadership, mentorship, or direct team collaboration styles are not explicitly detailed in the provided data.