Pragya P.

Pragya P.

Indore, India
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About Me

Senior MLOps & Data Infrastructure Engineer with 5+ years of experience architecting end-to-end production environments for AI/ML workloads. Expert in Model Deployment and Monitoring using AWS SageMaker, including managing real-time endpoints, batch inference, and model versioning via SageMaker Model Registry. Highly proficient in Infrastructure as Code (Terraform & CloudFormation) for automating secure, scalable cloud resources. Proven track record in building high-throughput ETL pipelines with AWS Glue and Airflow to deliver ML-ready datasets while ensuring operational excellence through CloudWatch performance monitoring.

AI, ML & LLM

AI/ML Apache Airflow LLMs

Backend

Python REST APIs Django FastAPI

Database

MySQL PostgreSQL SQLAlchemy MSSQL SQL DBT Oracle

DevOps

Azure Docker Cloud Functions Terraform CloudWatch GCP CI/CD Pipelines EC2 Prometheus AWS

Workflow

Git GitHub Actions GitLab CI

Other

Data Modeling Functional programming Regex Redshift BigQuery Caching Informatica Lambda Numpy Query Optimization Athena DataDog Matillion RabbitMQ Kinesis Prefect IAM Kafka Partitioning Talend Clustering Data Governance Data pipelines Dimensional Modeling Pandas Performance Tuning Quicksight S3 Data Engineering Data Transformation Multiprocessing Snowflake Agile SageMaker

Work history

UpStack
UpStack
Data & MLOps Engineer
2025 - Present (1 year)
Remote, [object Object]

  • Architected and managed AWS SageMaker deployment lifecycles, utilizing Model Registry for version control and deploying real-time endpoints for backend integration.

  • Implemented proactive Model Monitoring strategies using Amazon CloudWatch to track latency, throughput, and CPU utilization, ensuring high-availability operational SLAs.

  • Developed Terraform and CloudFormation scripts to automate the provisioning of SageMaker endpoints and supporting data infrastructure, reducing configuration errors.

  • Designed scalable ETL pipelines using AWS Glue and Airflow to transform unstructured data into curated, ML-ready datasets.

  • Automated batch jobs and data validation layers using AWS Batch and Python for large-scale analytical processing.

  • Developed Terraform scripts for provisioning AWS infrastructure components.

  • Collaborating with data scientists to deliver curated datasets for AI/ML use cases.

PythonSQLAlchemyAWS GlueRedshiftLambdaAthenaAirflowDockerTerraformAWS Batch AI/ML ETL Pipelines
ZecData Technology
ZecData Technology
Data Engineer
2023 - 2024 (1 year)
Indore, India, [object Object]

  • Built ETL workflows to ingest large-scale datasets from legacy Oracle/Salesforce systems into AWS Redshift for cloud-based analytics.

  • Utilized DBT for complex data transformation and cleansing, ensuring high data quality for downstream AI-driven solutions.

  • Managed real-time ingestion pipelines and automated data refresh cycles using Python and AWS Lambda.

PythonDBTPostgreSQLPandasNumpyAWS S3LambdaRedshiftDockerData Transformation Data Cleansing
Accenture
Accenture
Associate Data Engineer
2021 - 2022 (1 year)
Bangalore, India

  • Developed and optimized SQL queries for data extraction, aggregation, and reporting.

  • Created and maintained ETL scripts in Python to automate data refresh cycles.

  • Collaborated with analysts to deliver clean, structured data for visualization tools.

PythonSQLOracleMSSQLAWS EC2S3PandasETLPerformance Tuning AWS

Showcase

Zid
Zid

Automated ML Data Platform Managed a unified analytical ecosystem by executing Redshift SQL tuning for large queries and developing ELT workflows with DBT. Leveraged Terraform for full-stack infrastructure automation and deployed AWS Lambda for real-time data ingestion and system alerting. Integrated SageMaker endpoints with web interfaces via Django to provide real-time AI-driven business insights.

Education

BCA
BCA
IES College of Technology Bhopal - India
2017 - 2020 (3 years)