Prashant R. — AI Researcher

Passionate Machine Learning Engineer professional with 9 years of industry experience in Pharmaceutical, Telecommunication, Public sector and Automobile domain.Throughout my professional journey, I've developed MLOps Pipeline, Data Processing Frameworks, Data Ingestion Frameworks, Data Pipeline Implementation, Data Lake, Data Warehouse all aimed at tackling various business challengesTechnical Skills• Language – Python, Pyspark, Scala, C++, SQL, Advanced SQL, Unix Shell Scripting. • Data Base – Oracle, PostgreSQL, MySQL, Cassandra, DynamoDB, Hive, Redshift, Presto, Snowflake, NoSQL.• AWS Services – S3, Lambda, EMR, Glue, EC2, RDS, Quicksight, Kinesis, Sagemaker, Athena.• Azure Services - Azure Data Factory(ADF),Azure Synapse Analytics, Databricks• GCP Service - Vertex AI Pipelines, Model Registry, Experimentation and Training• Orchestration Tools – Apache Airflow, Cron, ControlM.• ML models - XGBoost with SMOTE , LightGBM, CatBoost, Nixtla Framework, Prophet etc• Big Data Tools – Databricks, DBT, Deltalake, Microsoft Fabric, Hadoop, Apache Spark, Spark Streaming • CI/CD – GitHub, Gitlab, BitBucket, Bamboo, Docker, AWS ECS, Terraform.Certifications• AWS Certified Solution Architect Professional (11/2023 -11/2026)• AWS Certified Solution Architect Associate (12/2020 - 11/2026)• AWS Certified Data Analytics – Specialty (04/2021 - 04/2024)• SnowPro Core Certified -Snowflake (05/2021 - NA)• Microsoft certified: Azure Data Engineer Associate (08/2021 - 08/2022)• AWS Certified Cloud Practitioner (06/2020 - 11/2026)• AWS Certified Developer Associate (02/2021 - 02/2024)• Microsoft certified: Azure Data Fundamentals (07/2021 - 07/2022)• Microsoft certified: Azure Fundamentals (08/2021 - 08/2022)• Microsoft certified: Azure AI Fundamentals (08/2021 - 08/2022)• Microsoft Certified: Fabric Analytics Engineer Associate (07/2024 - 07/2025)I am passionate about learning new technologies and applying them to real-world scenarios. I aspire to be a leader and innovator in the field of data science and cloud computing. You can reach out to me for opportunity on prashantram.90@gmail.com

Stackforce AI infers this person is a Data Engineering and MLOps expert in the SaaS industry.

Location: Mumbai, Maharashtra, India

Experience: 9 yrs 7 mos

Skills

Mlops
Machine Learning
Data Engineering
Cloud Computing
Etl
Database Management

Career Highlights

Expert in architecting scalable MLOps pipelines.
Proven track record in optimizing data processing frameworks.
Strong leadership experience in cross-functional team collaboration.

Work Experience

Fractal

Mlops engineer (2 yrs 7 mos)

Senior Engineer (2 yrs 1 mo)

Deloitte India (Offices of the US)

Consultant (1 yr)

Tata Technologies

Big Data Developer (1 yr)

Accenture

Big Data Engineer (2 yrs 11 mos)

Education

Bachelor's degree at University of Mumbai

Prashant R.

AI Researcher

Mumbai, Maharashtra, India9 yrs 7 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in architecting scalable MLOps pipelines.
Proven track record in optimizing data processing frameworks.
Strong leadership experience in cross-functional team collaboration.

Stackforce AI infers this person is a Data Engineering and MLOps expert in the SaaS industry.

Contact

prashant.ram@fractal.ai LinkedIn

Skills

Core Skills

MlopsMachine LearningData EngineeringCloud ComputingEtlDatabase Management

Other Skills

AWS CodePipelineAWS LambdaAWS S3AWS SDKAWS SageMakerAWS Step FunctionsAmazon AthenaAmazon EC2Amazon ECSAmazon KinesisAmazon QuickSightAmazon RedshiftAmazon Relational Database Service (RDS)Amazon Web Services (AWS)Analytical Skills

About

Experience

9 yrs 7 mos

Total Experience

2 yrs 4 mos

Average Tenure

4 yrs 8 mos

Current Experience

Fractal

2 roles

Mlops engineer

Promoted

Nov 2023 – Present · 2 yrs 7 mos · Mumbai, Maharashtra, India

Leading a team of 4 developers to prepare features which helped in reducing the operations call volume by 60%.
Architected scalable Vertex AI MLOps pipelines with Model Registry and Feature Store, productionizing 50+ demand forecasting models to deliver daily low-latency predictions that improved inventory optimization and customer experience at enterprise scale
Designed and implemented a Migration Framework leveraging the OpenAI API to automate the assessment and conversion of code, facilitating the transition from Teradata to Pyspark code compatible with Azure Databricks, with the ultimate objective of seamless migration to the Azure cloud platform.
Orchestrated the development and execution of targeted communication campaigns to engage the designated audience which improved customer experience score.
Collaborated with cross-functional teams to gather insights and develop streamlined feature set, resulting in churning model accuracy improvements over time.

Stakeholder ManagementApache SparkMLOpsMachine Learning

Senior Engineer

Oct 2021 – Nov 2023 · 2 yrs 1 mo · Mumbai, Maharashtra, India

Built a production-grade AWS SageMaker MLOps pipeline for churn prediction (XGBoost), implementing automated model lifecycle management, monitoring, and cost-optimized training at scale.
Developed MLOps Solution end to end pipeline on Azure Databricks and MLflow. The solution included experiment tracking, drift detection, data validation and monitoring dashboard.

RAGAWS LambdaMLOpsMachine Learning

Deloitte india (offices of the us)

Consultant

Oct 2020 – Oct 2021 · 1 yr · Mumbai, Maharashtra, India

Engineered and implemented a robust Data Processing Framework to streamline the development of ETL pipelines which significantly reduced development time, enabling other developers to create ETL pipelines with greater efficiency.
Developed a customized Crawler script using AWS SDK to automate the crawling of AWS S3 paths which automated the data discovery process, reducing manual effort and ensuring accurate cataloging in AWS Glue Data Catalog.
Automated cluster provisioning and configuration using AWS CodePipeline that reduced manual intervention, ensuring consistency across cluster environments and minimizing the risk of errors
Innovated and implemented a Metadata Extractor script for dynamically generating SQL insert statements from AWS RDS metadata tables.
Leveraged Python testing frameworks (pytest, pydantic, unittest, mock) to enforce data integrity, reducing errors by 50% and improving pipeline reliability.
Conducted in-depth analysis of Spark jobs to identify performance bottlenecks and areas for optimization which resulted in a remarkable 40% reduction in processing time.

AWS CodePipelineData StructuresData EngineeringETL

Tata technologies

Big Data Developer

Oct 2019 – Oct 2020 · 1 yr

Designed and implemented a robust Data Ingestion Framework for seamless extraction and transfer of data from Oracle to AWS S3 that significantly eased the migration of data from Oracle databases to AWS S3, contributing to the success of the migration activity.
Engineered a Python script for the creation of Transient EMR Clusters, optimizing costs for ETL workloads and achieving a 45% reduction in operational cost without any data loss.
Developed custom script for loading nested JSON files into AWS S3, incorporating data wrangling techniques and adding derived columns to meet business requirements and facilitated downstream analytics.
Collaborated closely with the Data Science team on the development of a Random Forest Machine Learning model to predict vehicle mileage with an accuracy of 90%.

Data StructuresPython (Programming Language)Data EngineeringETL

Accenture

Big Data Engineer

Nov 2016 – Oct 2019 · 2 yrs 11 mos · Mumbai, Maharashtra, India

Performed data cleansing and transformation process by working with diverse data sources including CSV, text, Avro, and Parquet files, using PySpark to enforce required business logic.
Designed and implemented ETL pipelines using PySpark to perform Change Data Capture (CDC) from diverse sources providing accurate and up-to-date data for downstream analytics.
Developed a Shell Script for comprehensive Hive metadata backup, housekeeping, and data archival processes on AWS S3 that enhanced the integrity of Hive metadata and optimized storage through housekeeping.
Collaborated with the Security team to implement Apache Knox as a reverse proxy, enhancing security by hiding the original Spark Web UI URL.
Gave KT to fresher about the functionality and working of ETL workflows.

Apache SparkData StructuresData EngineeringETL