Rupali Shisode

Lead Data & AI Engineer with 8.9+ years of experience architecting and scaling enterprise data platforms, machine learning infrastructure, RAG frameworks, and agentic workflows across healthcare, life sciences, and fintech domains. Specialized in unifying predictive ML and LLM capabilities with AWS-native data engineering (AWS Glue, Python, PySpark, S3, Step Functions, Bedrock, LLM, Lambda, IAM, SNS). Proven track record in constructing metadata-driven ingestion pipelines, building production-grade Agentic RAG solutions, automating MLOps/AgentOps lifecycles, and establishing robust AWS Lake Formation governance.

Expert at driving cross-functional engineering teams to transform enterprise data into secure, resilient, and business-ready AI products.

Senior Data & AI Engineer - Tata Consultancy Services

(2026-02)

Client: Vanguard, USA

• Data Engineering & Lakehouse Pipelines: Designed and built highly scalable data pipelines using Python, PySpark, AWS Glue, Lambda, IAM, SNS and S3. Cleaned, processed, and structured high-volume enterprise data to support downstream Data Science and Natural Language Processing (NLP) initiatives.
• GenAI & RAG Implementation: Developed Retrieval-Augmented Generation (RAG) pipelines and GenAI features in production using Amazon Bedrock, LLMs, prompting & AI evaluation, LLM and LangChain.
• Embeddings & Vector Databases: Integrated embedding models and Vector Databases (Amazon OpenSearch, Chroma DB) to build scalable semantic search capabilities and enterprise knowledge bases.
• CI/CD & System Optimization: Automated data pipeline and AI model deployments using GitHub Actions. Optimized system scalability and performance while enforcing enterprise security and data governance guardrails.
• Cross-Functional Collaboration: Partnered closely with data scientists, software engineers, and domain experts to bridge the gap between complex data infrastructure and advanced AI solutions.
• AI-Assisted Development: Boosted personal and team coding efficiency, accelerating code reviews and pipeline development by 30% using AI assistants like GitHub Copilot and Claude Code.

AI Data Engineer - Roche Information Solutions

(2020-12 - 2026-02)

Projects: NaCore, NaPathology, NaPoc, Navify Integrator, PandiHub

• Data Engineering Pipelines: Built automated ETL pipelines using Python, PySpark, AWS S3, AWS Glue, AWS Lambda, SNS, and IAM to clean, chunk, and transform unstructured healthcare data into structured knowledge bases for Retrieval-Augmented Generation (RAG) applications.
• AWS AI/ML Pipeline Integration: Orchestrated data flows connecting secure data lakes to AWS AI/ML services like (Bedrock, Sagemaker), ensuring low-latency and reliable data delivery for LLM, prompt engineering and model inference.
• Data Architecture for AI/LLMs: Engineered scalable data pipelines on AWS (S3, AWS Glue, AWS Bedrock, Athena) to unify and process complex healthcare datasets, laying the foundation for both predictive machine learning and Large Language Model (LLM) workflows.
• Performance Optimization: Optimized Apache Spark and AWS Glue processing workflows, reducing data processing runtime by 35% and establishing automated event triggers for continuous model retraining.
• Data Quality & Monitoring: Developed automated data validation routines and anomaly detection scripts using Python and SQL to maintain high data integrity for RAG systems and prevent feature drift in production ML models.
• CI/CD & Cross-Functional Collaboration: Configured GitLab CI/CD pipelines for automated testing and deployment of Python-based data pipelines; collaborated with clinical stakeholders to translate complex requirements into scalable AI-driven data solutions.

Data Engineer (Software Engineer) – Data Engineering & Management - Saama Technologies

(2019-11 - 2020-12)

Project: Life Science Analytics Cloud CDO2.2 | Clients: Kiniksa, Astex, Mallinckrodt

• Clinical ETL Pipelines: Developed scalable ETL workflows using PostgreSQL and AWS Redshift to ingest, clean, and standardize clinical trial data across multi-site sponsors.
• SQL/Spark Tuning & Migration: Migrated legacy processing scripts to PySpark/SparkSQL on AWS, optimizing query execution plans and resource utilization for large-scale datasets.
• Pipeline Reliability & Monitoring: Established automated execution logs, proactive alerting, and validation scripts to maintain 99%+ uptime across high-priority clinical data pipelines.

ETL + SQL Developer (Associate Software Engineer) - Figmd India Pvt. Ltd.

(2017-10 - 2019-11)

Project: American College of Emergency Physicians (ACEP) Registry USA

• Healthcare Data Processing: Ingested, transformed, and validated physician-level healthcare data for CMS-compliant MIPS reporting using MS SQL Server and Oracle.
• Database Engineering: Authored modular SQL views, stored procedures, and schema migrations to support high-throughput data processing.
• Process Automation: Built Java-based automated ingestion utilities, reducing client data onboarding turnaround time by 20%.

B.E. - Computer Engineering - Pune University (2017)

Diploma - Computer Engineering - MSBTE (2014)

SSC - Maharashtra StateBoard (2011)