In the dynamic world of artificial intelligence, deep learning stands as one of its most groundbreaking subsets.

As industries increasingly recognize its transformative potential, the demand for competent deep learning engineers surges. But what sets apart an outstanding deep learning engineer resume from a generic one?

Whether you're dipping your toes into the world of neural networks for the first time, or you're a mid-level professional aiming to refine your professional presentation, knowing how to articulate your skills and experiences is paramount.

This resume sample is here to navigate those waters with you. Dive in with us, as we break down the key components of a compelling resume that not only captures attention but ensures you're seen as the right fit for the job.

Deep Learning Engineer Resume

Tips & Guides
Vishal Sinha
Deep Learning Engineer
3+ years experienced deep learning professional adept at developing ML models by deploying DL packages. Possesses diverse experience of processing huge datasets while designing deep learning network architectures & models to assess investment portfolios. Proficient in coordinating with the engineering teams to execute & successfully deliver research-oriented projects, and conceptualizing & implementing back-testing investment strategies to maximize business returns.
  • Deep Learning Frameworks: Tensorflow, Keras, Caffe, Torch, PyTorch, Deeplearning4j, MXNet, Microsoft Cognitive, Toolkit, Neural Network Deep-dive, RBM, Tflearn, Keras, Deep Networks
  • Tools/Languages: Spark, SparkR, R, Python, Scala, Hive, SQL, SAS, Tableau, SPSS, Hadoop, Stata, Google Analytics, C++
  • Platforms & Systems: Git/GitHub, Node.js, Firebase
  • Frameworks, Libraries & Database: Django, React, jQuery, .NET, MongoDB, MySQL, PostgreSQL
Post Graduate Certificate Programme in Deep Learning & Artificial Intelligence
IIT Ropar
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    Bachelor of Technology in Computer Science
    Indian Institute of Technology (IIT), Delhi
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      Deep Learning Engineer
      Surgesystems Limited
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        DL Model Development & Project Delivery
        • Utilizing deep learning packages like Tensorflow, H2O, lighGBM. Theano, Spark MLlib, etc. to develop ML models
        • Assisting with research-oriented & analytical projects by deploying advanced computational, ML, and DL algorithms
        • Coordinating with the engineering team of 25 to process huge datasets & incorporate the models on the platform
        • Developing deep learning network architectures & DL-based models to evaluate and assess investment portfolios
        Data Processing & Troubleshooting
        • Performing data aggregation & preprocessing structured/unstructured data in the form of images, text, etc.
        • Executing statistical/conceptual error analysis of outputs/predictions from ML models & data processing techniques
        • Designing prototypes by manipulating and analyzing complex & high-volume of data from various sources
        Data Flow Management & Model Visualization
        • Devising statistical validation methods to ensure the right kind of data flow into the AI models and user interfaces
        • Testing infrastructure requirement & processing models speed to make it suitable for real-time processing
        • Building sophisticated visualization for model output to integrate into the processing pipeline
        Strategy & Risk Management
        • Conceptualizing & implementing back-testing investment strategies to maximize business returns for clients
        • Scrutinizing investment behavior to support portfolio managers and preventing frauds & cases of mishaps
        Data Analysis & Research
        Herolutions Technologies
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          • Monitored key metrics and understood root causes of changes in metrics to identify opportunities for improvement
          • Analyzed data by deploying statistical methods to generate 15+ useful business reports
          • Collaborated with product managers in exploring data to find actionable insights & make recommendations through:
          • Funnels, cohort analysis, long-term trends, user segmentation, regression models, etc.
          Key Project:
          • Tech Stack: R, Python, Power BI
          • Rendered consultancy services for India's largest spirits manufacturer to identify key focus outlets in a region
          • Deployed methodologies like Data Exploration & Descriptive Analysis, correlation analysis, clustering & segmentation
          • Assisted in developing the Prioritization & Propensity model via Predictive Analysis, logistic regression & other statistical techniques

          Check out our similar blogs on:
          Machine Learning Resume
          Data Scientist Resume

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