Abhishek Srivastava

AI Researcher

India3 yrs 3 mos experience

Most Likely To Switch

Key Highlights

Expert in machine learning and predictive modeling.
Proven track record in FinOps and RPA solutions.
Strong experience with AWS and MLOps tools.

Stackforce AI infers this person is a Machine Learning Engineer specializing in Fintech and Data Science.

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Skills

Core Skills

Machine LearningData AnalysisData Science

Other Skills

AWS SageMakerAmazon Web Services (AWS)Automation AnywhereBatch ProcessingBlue PrismC++CommunicationComputer ScienceData CleaningData ModelingData PreprocessingDeep LearningFront-End DevelopmentMLOpsModel Deployment

Experience

Auxiliobits

Machine Learning Engineer

Sep 2023 – Present · 2 yrs 6 mos · Mohali district, Punjab, India · On-site

FinOps
Spearheaded a dynamic FinOps Recommendation project, leveraging advanced Time Series Forecasting techniques and
TensorFlow for predictive analysis.
Developed and implemented robust models for forecasting future utilization of key resources, including CPU, Memory,
and
Disk, based on thorough analysis of historical data.
Collaborated with cross-functional teams to ensure seamless integration of the predictive models into the existing
infrastructure, fostering a more proactive approach to resource management.
Demonstrated expertise in leveraging machine learning and statistical methodologies to extract actionable insights,
enhancing
the efficiency of resource utilization in alignment with FinOps principles.
RPA
Designed and implemented end-to-end Robotic Process Automation (RPA) solutions to streamline business processes and
enhance operational efficiency.
Collaborated with business analysts and stakeholders to gather requirements and identify automation opportunities
within
various departments.
Developed automation workflows using leading RPA tools such as UiPath, Blue Prism, Automation Anywhere, ensuring
adherence to best practices and industry standards.
Implemented error handling mechanisms and conducted rigorous testing to guarantee the reliability and accuracy of
automated processes.

TensorFlowTime Series ForecastingRobotic Process Automation (RPA)Machine LearningStatistical MethodologiesData Analysis

Quantiphi

Machine Learning Engineer

Aug 2022 – May 2023 · 9 mos · Bengaluru, Karnataka, India

I worked on production project on my company where we Implemented a machine learning solution on AWS
SageMaker for insurance churn prediction, accurately forecasting customer attrition and renewals, optimizing
retention strategies and enhancing overall customer satisfaction. Here is the process I followed:
Data Preprocessing: I prepared the training data by performing necessary preprocessing steps such as handling missing
values, encoding categorical variables, and splitting the data into training and validation sets. Training the XGBoost
Model, Saving the Trained Model, Developed an inference pipeline using this trained model, which can be used to
predict whether a new customer will renew their policy or not. SageMaker Batch Transform, SageMaker Batch
Inference, Deploying the Model.
MLOps
.I have experience with MLOps (Machine Learning Operations) tools, which are designed to streamline and automate
the deployment, monitoring, and management of machine learning models in production environments. Here's how I
would describe my experience with MLOps tools:
I have hands-on experience with various MLOps tools that facilitate the end-to-end ML model lifecycle, from
development to deployment and maintenance. These tools enable efficient collaboration among data scientists,
developers, and operations teams, ensuring the seamless integration of machine learning models into production
systems.
I worked on Git and Git-based platforms (such as GitLab and GitHub) to version control machine learning models, AWS
CloudWatch for monitoring the performance and health of deployed machine learning models, Model monitoring,
Model serving, Model validation, AWS Sagemaker pipeline, S3, ECR, Model deployment.