Experienced Machine Learning Engineer with a proven track record in developing and implementing cutting-edge AI solutions across diverse industries including Sales Technology, Conversational AI, FinTech, and HealthTech/Healthcare. Proficient in Deep Learning, Neural Networks, Natural Language Processing (NLP), Computer Vision, Large Language Models (LLMs like ChatGPT and Claude), Generative AI, Prompt Engineering, Reinforcement Learning, and RLHF. Expertise in model optimization techniques including distillation, quantization, training and fine-tuning, and LoRA. Skilled in working with large-scale text, image, and video datasets. Experienced in leading end-to-end ML projects from conceptualization to production deployment, with a strong background in MLOps and the complete 0-1 journey of bringing products and features to life. Demonstrated ability to lead both R&D ML projects and production implementations, developing scalable solutions for high-impact domain problems and optimizing ML models for production environments. Technical proficiencies include Python, FastAPI, MongoDB, AWS, TensorFlow, PyTorch, Hugging Face, and other modern ML frameworks. Possesses significant software development and design experience, adept at leveraging various tech stacks to create robust, production-ready solutions. Notably, has substantial experience working in early-stage startup environments, including being part of the seed to Series A journey, providing invaluable insights into rapid scaling and agile development in resource-constrained, high-growth settings.#MachineLearning #AI #DeepLearning #NLP #ComputerVision #LLMs #GenerativeAI #MLOps #Python #AWS #Startup
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Machine Learning EngineerPaypalSan Jose, Ca, Us -
Senior Machine Learning EngineerSybill Sep 2023 - PresentMountain View, California, United StatesCollaborating cross-functionally to drive continuous improvement in AI model performance, focusing on reducing hallucinations, improving factuality, and enhancing overall system reliability in a fast-paced startup environment, throughout the seed to Series A journey. Architecting and implementing cutting-edge AI solutions for sales intelligence, leveraging Generative AI, LLMs, and advanced NLP techniques, for 1000s of users across 100s of organizations with industry-defining accuracy.Leading the technical implementation of an end-to-end meeting intelligence platform, integrating various ML models and services into a cohesive system. Utilize a modern tech stack including Python, FastAPI, MongoDB, and AWS to build scalable and robust infrastructure.Developed innovative meeting summarization modules using domain-specific fine-tuning and prompt engineering to extract key outcomes, next steps, and personalized insights.Engineered a sophisticated RAG pipeline for deal intelligence, integrating multiple data sources and implementing novel querying mechanisms. Utilized state-of-the-art embedding techniques and recency-biased retrieval to surface highly relevant information from CRM systems and meeting transcripts.Led 0-1 development of innovative AI email feature that is a differentiator in a competitive market, which has unprecedented 11% initial adoption rate. -
Machine Learning EngineerGot It Ai Feb 2022 - Aug 2023San Mateo, California, United StatesBuilt and improved multiple iterations of core product offering – generative customer service bot that takes in a set of documents and builds a conversational experience around it. Integrated novel UX features by extensively working with LLMs: prompt engineering, model training, fine-tuning and distillation, RLHF, QLoRA etc. to bring in multiple revenue streams.Developed and implemented multiple iterations of core IP: novel automated conversational flow discovery algorithms, by solving complex engineering problems to discover conversational flows from conversation datasets.Developed and improved novel conversational flow evaluation method to reduce running time.Brought generative AI features to core product offering to improve user experience.Built data pipeline to add and perform data processing steps (classification, entity tagging, audio transcription etc.) in an automated manner using airflow. Significantly reduced overhead of processing user data for downstream tasks.Collaborated with technical and business facing colleagues to solve challenges with LLM-based product development including hallucination detection, factuality dataset creation, annotating specifications for RLHF etc.Project ownership to rapidly evaluate, prototype, and build new feature and product offerings using latest ML and LLM technologies and tools for business development and revenue streams in a startup ecosystem. -
Machine Learning EngineerFinra Nov 2019 - Feb 2022Rockville, Maryland, United StatesWorked on Natural Language problems, in both deliverable and R&D projects.Gained experience with AWS environment and tools (S3, EC2, KMS, SQS, SageMaker etc.).Integrated technologies like Docker and Spark for scalability.Practiced industry-standard software development practices: git-code versioning and collaboration, unit testing.Leveraged advantages of transformer architectures.Worked on text similarity tasks at various levels of large text documents.Investigated approaches for making NLP models robust against adversarial attacks.Solved task-specific end-to-end document entity redaction, on complex datasets.Worked on explainable deep learning models using SOTA techniques like Integrated Gradients/Captum, gradient visualizations, attention layer visualizations. -
Machine Learning Research AssistantJohns Hopkins University & Medicine Feb 2019 - Nov 2019Baltimore, Maryland AreaJohns Hopkins Malone Center for Engineering In HealthcareWorking on segmenting phases of cataract surgery videos.Using coordinate labels to train deep architectures for image segmentation.Creating image and text datasets for time series modeling.Working with deep learning models – CNNs, RNNs, LSTMs.Using transfer learning for fine tuning architectures to act as feature extractors.Implementing SOTA models from research papers, exploring techniques to improve model performances, running experiments and evaluating model results.
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Data ScientistJohns Hopkins Medicine Jun 2018 - Dec 2018Baltimore, Maryland AreaDepartment of Anesthesiology & Critical Care MedicineDeveloped framework to identify physiologically meaningful ICU patient data, and predict clinical outcomes.Mimic-III Matched Waveform Database was used that contained ICU Electronic Health Records of variable length time series vital signs signals (like heart rate, BP, SpO2 etc.), sampled at 0.01667 and 1 Hz.Worked in collaboration with clinical experts to develop artifact correction algorithm, which when applied for data cleaning and prepossessing to the time series data, gave approximately 20,000 physiologically viable ICU stay records belonging to 10,000 patients, for 8 considered signals of interest.Developed predictive analytics algorithms in Python (package) to classify certain transitional and quality improvement-related clinical outcomes of interest using various classification methods.Institute for Data Intensive Engineering and Science (IDIES) 2018 poster presentation: Developing a Framework to Enable Large-scale Analysis of Physiologic and Clinical Data Obtained through Electronic Health Records, Ali Afshar, Digvijay Singh, Aidan Crank -
Research AssistantJohns Hopkins University & Medicine May 2017 - Mar 2018Baltimore, Maryland AreaDepartment of Applied MathematicsModified PATH algorithm to adopt seeding (seeded PATH or sPATH).Implemented sPATH in C++ using PATH from GraphM package.Ran experiments to compare memory and time complexity with sPATH and SGM.
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Software Engineering InternDefence Research And Development Organisation (Drdo) May 2015 - Jul 2015New Delhi Area, IndiaInstitute for System Studies and AnalysesWorked on war game simulation software.Developed intelligent rule engine in Java (Eclipse IDE). -
Software Engineering InternInde Systems India Private Limited Nov 2014 - Dec 2014New Delhi Area, IndiaHuman Computer Interaction project.Developed real-time LED blink detection with Arduino Uno, using NeuroSky Mindwave brainwave technology.Developed blink detection algorithms, implemented in Objective-C using Xcode IDE and Mindwave APIs.
Digvijay S. Education Details
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Master'S Degree -
Master’S Degree -
Bachelor’S Degree -
Modern School, Barakhamba Road
Frequently Asked Questions about Digvijay S.
What company does Digvijay S. work for?
Digvijay S. works for Paypal
What is Digvijay S.'s role at the current company?
Digvijay S.'s current role is Machine Learning Engineer.
What schools did Digvijay S. attend?
Digvijay S. attended Johns Hopkins Whiting School Of Engineering, The Johns Hopkins University, Delhi Technological University (Formerly Delhi College Of Engineering), Modern School, Barakhamba Road.
Who are Digvijay S.'s colleagues?
Digvijay S.'s colleagues are Nate Tagles, Abhishek V., Roy Green, Terrance Tyrone Hopper, Ganeshan Malhotra, Danish S, Saransh Maheshwari.
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Digvijay S.
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