Saurav Jha

Senior Technical Architect with over 14 years of experience in software engineering, focused on developing and scaling production-grade, event-driven systems using Python, AWS, and Node.js. Demonstrates deep expertise in cloud architecture, designing scalable and resilient solutions on AWS. Known for leading high-performing teams to achieve technical excellence and deliver impactful user experience in dynamic environments.

Aspiring AWS certified solutions architect

• TensorFlow
• Scikit-learn
• SciPy
• Hugging face
• GenAI models
• MLflow/Kubeflow

FICS is a web-based, end-to-end platform designed to streamline clinical trials in life science domain, making them faster, easier, and more quality.

FICS consists of the following modules:

1. CTMS (Clinical Trial Management System)

Designed for sponsors and CROs, this application streamlines administrative tasks across the Study Startup, Conduct, and Closeout phases of clinical trials. The CTMS solution offers unified access to study data, allowing teams to manage multiple trials seamlessly. It also integrates with EDC for efficient patient data collection.

2. Trial Source Data Application(TSDA)

The TSDA application is designed for clinical trial sites (hospitals/clinic facilities), enabling site users to perform and manage site-specific tasks related to clinical trials efficiently.

3. eTMF electronic Trial Master File

eTMF Enables sponsors to upload, manage, and retrieve clinical trial documents at study, country, and site levels, ensuring proper record collection throughout the trial lifecycle.

Tech Stack: ReactJS, Python Fast API, SQL Model, Microservices, Kafka, PostgreSQL, GraphQL, Opensearch, traefik, MinIO, AWS S3 Bucket, docker, GitLab CI/CD pipeline, ECS, ECR, Node.js, Express

Spark HA-Adapter:

● Worked with the Cloudera team during the feasibility study and implementation phase.

● Developed a SaaS-based product with multi-customer support, designed as an extensible utility using Apache Livy to enable remote start, restart, and status reporting for Spark jobs across clusters.

● implemented custom logic in Python and Django to handle failover scenarios, ensuring job continuity in case of a primary cluster failure. Processed and stored streaming data to support business decision-making.

● Automated the manual process of submitting and monitoring Spark jobs on the Hadoop cluster, saving approximately 1,000 hours per quarter.

Tech Stack: Python Django framework, Rest APIs, Redis, PostgreSQL, Angular, TypeScript, Docker, Kubernetes, Cloudera, Spark, Jfrog Artifactory, GitLab CI/CD pipeline

Supply Chain Management:

An e-marketplace built to streamline procurement and supply chain needs, replacing Dell's legacy applications that struggled with complex business requirements and high maintenance costs.

Tech Stack: Node.JS, Type ORM, PostgreSQL, AWS Lambda, S3 Bucket, API Gateway, SNS, SQS, Step functions, DynamoDB, Micro Services, Gitlab, Angular, TypeScript.

Protect 4.0

Protect 4.0 is a web-based application for flash memory bad block testing, using machine learning to detect potential bad blocks and optimize factory operations across regions.

Tech Stack: Python Flask, SQLAlchemy ORM, Kafka, ARIMA model,AWS S3 bucket, RDS PostgreSQL, Redshift , Angular, TypeScript, CI/CD- Bitbucket, Docker, JFrog Artifactory, Spinnaker, Mesosphere DC/OS.

DPS Market Harmonization

It is a decision-support system for product sales forecasting, helping organizations analyze competitors and customer bases. Improved forecast accuracy increased ROI. I worked with the data science team to integrate ARIMA machine learning models.

Tech Stack: Python Django Rest API, Microservices, Kafka, HTML5, Angular8, TypeScript, AWS Lambda function, S3 Bucket, Boto3, RDS PostgreSQL, API Gateway, Docker, EKS, Splunk, Gitlab