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% Author: Rakesh Bal
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\begin{document}

%----------HEADING----------
\begin{center}
    {\Huge \scshape Rakesh Bal} \\ \vspace{1pt}
    \small \raisebox{-0.1\height}\faPhone\ (424) 440-9976 ~
    \href{mailto:rakeshbal99@gmail.com}{\raisebox{-0.2\height}\faEnvelope\  \underline{rakeshbal99@gmail.com}} ~
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%-----------PROFESSIONAL SUMMARY-----------
\section{Professional Summary}
ML Engineer with 5+ years of experience in production ML systems, LLM development, and applied AI research. Published at NeurIPS, AAAI, and ICWSM with 56+ citations. Track record of deploying ML at scale: 15-30\% accuracy improvements, 99.89\% pipeline uptime, and systems serving 50K+ daily users. Experienced in LLM fine-tuning, distributed training, and end-to-end MLOps. Strong foundation in responsible AI through published work on misinformation detection and algorithmic fairness.

%-----------EDUCATION-----------
\section{Education}
  \resumeSubHeadingListStart
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      {University of California Los Angeles (UCLA)}{Sep 2021 -- Jun 2023}
      {Master of Science in Computer Science | GPA: 3.81/4.0}{Los Angeles, CA}
    \resumeSubheading
      {Indian Institute of Technology (IIT) Kharagpur}{Jul 2016 -- May 2020}
      {Bachelor of Technology in Computer Science \& Engineering | GPA: 9.04/10.0}{Kharagpur, India}
  \resumeSubHeadingListEnd

%-----------EXPERIENCE-----------
\section{Experience}
  \resumeSubHeadingListStart

    \resumeSubheading
      {Stovell AI Systems}{Jan 2024 -- Present}
      {Machine Learning Engineer}{San Francisco, CA}
      \resumeItemListStart
        \resumeItem{Architect end-to-end ML pipelines for model training, deployment, and monitoring, tracking both model accuracy and business ROI impact across multiple customers}
        \resumeItem{Design and train custom architectures (Transformers, LSTMs, CNNs, hybrids) for fuel price and volume demand forecasting, achieving 15-30\% MAE reduction}
        \resumeItem{Develop causal inference models for price-volume elasticity prediction; create evaluation frameworks measuring model performance and downstream business impact}
        \resumeItem{Build production ETL pipelines for data ingestion, processing, and model inference; maintain 99.89\% uptime SLA across all customer deployments}
        \resumeItem{Implement comprehensive monitoring with DataDog and Weights \& Biases for data quality validation, model drift detection, and accuracy tracking across geographies}
        \resumeItem{Conduct systematic model evaluation including robustness testing, edge case analysis, and failure mode identification before production deployment}
      \resumeItemListEnd

    \resumeSubheading
      {Amazon Web Services (AWS)}{Jun 2022 -- Sep 2022}
      {Applied Scientist Intern}{San Francisco, CA}
      \resumeItemListStart
        \resumeItem{Benchmarked large language models (ProtBERT, ProtT5, ProtGPT) for Drug-Target Interaction (DTI) prediction; integrated diffusion-based molecular docking}
        \resumeItem{Implemented data-parallel distributed training on P4d.24xlarge instances (8x A100 GPUs), achieving 2x memory efficiency through gradient checkpointing and mixed precision}
        \resumeItem{Improved baseline performance by 20\% and 12\% on two benchmark datasets using protein language model embeddings; results presented at Amazon ML Conference}
        \resumeItem{Developed systematic evaluation pipeline for model comparison including accuracy, inference latency, and computational cost tradeoffs}
    \resumeItemListEnd

    \resumeSubheading
      {Goldman Sachs}{Aug 2020 -- Sep 2021}
      {Software Engineer}{Bangalore, India}
      \resumeItemListStart
        \resumeItem{Led full-stack development integrating two internal bug tracking systems with migration to AWS, architecting scalable RESTful APIs serving 50,000+ daily users}
        \resumeItem{Built backend services with Java/Spring Boot and frontend with TypeScript/Angular; managed complete SDLC from requirements to production deployment}
        \resumeItem{Designed database schemas and optimized query performance for high-throughput issue tracking workflows}
      \resumeItemListEnd

    \resumeSubheading
      {Kanini Software Solutions}{Aug 2023 -- Jan 2024}
      {DevOps Engineer}{Los Angeles, CA}
      \resumeItemListStart
        \resumeItem{Automated infrastructure provisioning for healthcare platform using Terraform, improving deployment reliability with AWS S3, Lambda, and RDS}
      \resumeItemListEnd

  \resumeSubHeadingListEnd

%-----------PUBLICATIONS-----------
\section{Publications}
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      \resumeProjectHeading
          {\textbf{PGraphDTA: Improving Drug-Target Interaction Prediction using Protein LLMs and Contact Maps}}{NeurIPS 2023 Workshop}
          \resumeItemListStart
            \resumeItem{Rakesh Bal et al. | AI for Science Workshop | 5 citations}
            \resumeItem{Developed novel architecture combining protein LLM embeddings with structural contact maps for improved DTI prediction}
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          {\textbf{Two-Sided Fairness in Non-Personalised Recommendations}}{AAAI 2021}
          \resumeItemListStart
            \resumeItem{Rakesh Bal et al. | Student Abstract | 9 citations}
            \resumeItem{Proposed fairness-aware recommendation framework balancing user and item-side equity}
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          {\textbf{Analysing the Extent of Misinformation in Cancer Related Tweets}}{ICWSM 2020}
          \resumeItemListStart
            \resumeItem{Rakesh Bal et al. | 42 citations}
            \resumeItem{Created dataset and attention-based BiLSTM-CRF model for detecting health misinformation; contributed to responsible AI by identifying harmful content patterns}
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%-----------PROJECTS-----------
\section{Projects}
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          {\textbf{DeepSub: Fine-tuning LLMs for Multi-Source Subtitle Translation} $|$ \emph{QLoRA, Gemma-7B, LLaMA-7B}}{}
          \resumeItemListStart
            \resumeItem{Fine-tuned Gemma-7B and LLaMA-7B using QLoRA (4-bit quantization) for multilingual subtitle translation synthesizing context from 2 source languages}
            \resumeItem{Implemented triplet translation pipeline; evaluated on 6 Indic and 3 European languages with BLEU score tracking via Weights \& Biases}
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          {\textbf{CLIP for Visual Question Answering (VQA)} $|$ \emph{OpenAI CLIP, MCAN, Pythia}}{}
          \resumeItemListStart
            \resumeItem{Integrated OpenAI CLIP with VQA architectures (MCAN, Pythia) achieving 2\% improvement in both zero-shot and fine-tuned settings on VQA2.0}
            \resumeItem{Added Language-Driven Semantic Segmentation (LSeg) for improved counting questions}
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%-----------SKILLS-----------
\section{Skills}
 \begin{itemize}[leftmargin=0.15in, label={}]
    \small{\item{
     \textbf{LLM/GenAI}{: Transformers, LLM Fine-tuning (QLoRA/LoRA/PEFT), vLLM, LangGraph, HuggingFace, Prompt Engineering, Model Evaluation} \\
     \textbf{ML Frameworks}{: PyTorch, TensorFlow, Keras, scikit-learn, XGBoost} \\
     \textbf{MLOps}{: Docker, Kubernetes, KubeFlow, MLflow, Weights \& Biases, Distributed Training, Model Serving} \\
     \textbf{Cloud/Infra}{: AWS (SageMaker, EC2, S3, Lambda), GCP, Azure, Terraform} \\
     \textbf{Data}{: Snowflake, DBT, ETL Pipelines, DataDog, SQL} \\
     \textbf{Languages}{: Python, Java, C++, Go, SQL, TypeScript}
    }}
 \end{itemize}

%-----------ACHIEVEMENTS-----------
\section{Achievements}
 \begin{itemize}[leftmargin=0.15in, label={}]
    \small{\item{
     \textbf{IIT-JEE Advanced}{: All India Rank 265 out of 200,000 candidates} \\
     \textbf{IIT-JEE Mains}{: All India Rank 187 out of 1,300,000 candidates} \\
     \textbf{KVPY Fellowship}{: All India Rank 191 (prestigious national science scholarship)}
    }}
 \end{itemize}

\end{document}
